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# Extracted Content United Republic of Tanzania NATIONAL SAMPLE CENSUS OF AGRICULTURE 2007/2008 Volume 1: TECHNICAL AND OPERATION REPORT December 2011 i TABLE OF CONTENTS ACRONYMS ....................................................................................................................................... v PREFACE ........................................................................................................................................... vi CHAPTER 1 ....................................................................................................................................... 1 GENERAL BACKGROUND ........................................................................................................... 1 1.0 Background Information ................................................................................................ 1 1.1 Introduction .................................................................................................................... 1 1.2 Rationale of the 2007/08 Agricultural Sample Census.................................................. 2 1.2.1 Census objectives ........................................................................................................... 2 1.2.2 Census Scope and Coverage .......................................................................................... 3 1.3 Main Activities Undertaken ........................................................................................... 4 CHAPTER 2 ....................................................................................................................................... 5 METHODOLOGY ............................................................................................................................ 5 2.1 Census Organization ...................................................................................................... 5 2.2 Tabulation Plan Preparation .......................................................................................... 6 2.3 Sample Design ............................................................................................................... 6 2.4 Questionnaire Design and Other Census Instruments ................................................... 6 2.5 Field Pilot-Testing ......................................................................................................... 7 2.6 Training of Trainers, Supervisors and Enumerators ...................................................... 8 2.7 Information, Education and Communication (IEC) Campaign ..................................... 8 2.8 Data Collection .............................................................................................................. 8 2.9 Field Supervision and Consistency Checks ................................................................... 9 2.10 Data Processing and Analysis ........................................................................................ 9 2.10.1 Data entry ....................................................................................................................... 9 2.10.3 Tabulations .................................................................................................................. 10 2.10.4 Analysis and Report Preparation ................................................................................. 10 2.11 Data Quality Control .................................................................................................... 10 2.12 Funding Arrangements ................................................................................................ 11 CHAPTER 3 ......................................................................................................................................... 12 CENSUS ORGANIZATION .............................................................................................................. 12 3.1 General Overview ........................................................................................................ 12 3.2 Census Administration................................................................................................. 12 3.2.1 National level ............................................................................................................... 12 3.2.2 Regional level .............................................................................................................. 13 ii 3.2.3 District level ................................................................................................................. 13 3.3 Composition and Functions of the Technical Committee ........................................... 13 3.3.1 Agricultural Sample Census Technical Committee ..................................................... 13 3.3.2 Censuses and Surveys Technical Working Group ....................................................... 14 3.3.3 Information, Education and Communication (IEC) and Advocacy ............................ 15 3.4 Census Logistics .......................................................................................................... 15 3.4.1 Logistics team .............................................................................................................. 15 CHAPTER 4 ..................................................................................................................................... 17 SAMPLE DESIGN FOR THE CENSUS ....................................................................................... 17 4.1 Introduction .................................................................................................................. 17 4.2 Design of the National Master Sample ........................................................................ 17 4.3 Design of the National Sample Census of Agriculture ................................................ 17 4.4 Basic Formulae for Estimation .................................................................................... 22 4.5 EA Estimates: .............................................................................................................. 22 4.6 District Estimates – Rural ............................................................................................ 22 4.7 Regional Estimates ...................................................................................................... 23 4.8 National Estimates ....................................................................................................... 23 4.9 Adjustment ................................................................................................................... 23 4.10 Adjustment Factor for the Rural Sample ..................................................................... 24 CHAPTER 5 ..................................................................................................................................... 25 GENERAL TERMS, CONCEPTS AND DEFINITIONS ........................................................... 25 5.1 Important Considerations ............................................................................................. 25 5.2. Concepts and Definitions ............................................................................................. 25 5.2.1 Household and Holding ............................................................................................... 25 5.2.2 Holding Characteristics ............................................................................................... 26 5.3. Land access/ownership/tenure ..................................................................................... 26 5.3.1 Land Use ...................................................................................................................... 28 5.4 Livestock ...................................................................................................................... 29 5.4.1 Poultry.......................................................................................................................... 31 5.5 Irrigation ...................................................................................................................... 31 5.5.1 Drainage ....................................................................................................................... 31 5.5.2 Plot ............................................................................................................................... 31 5.6 Fertilizers and Pesticides ............................................................................................. 31 5.6.1 Fertilizers ..................................................................................................................... 32 5.6.2 Pesticides ..................................................................................................................... 32 iii 5.7 Large Scale Farms ....................................................................................................... 32 5.8 Small Scale Farms ....................................................................................................... 33 5.9 Operator ....................................................................................................................... 33 5.10 Masika Season ............................................................................................................. 33 5.11 Vuli Season .................................................................................................................. 33 5.12 Fish Farming ................................................................................................................ 33 5.13 Hunting and Gathering ................................................................................................ 33 5.14 Bee Keeping ................................................................................................................. 33 CHAPTER 6 ..................................................................................................................................... 34 AGRICUTURAL SAMPLE CENSUS PREPARATIONS .......................................................... 34 6.1 Introduction .................................................................................................................. 34 6.2 Design of the Census Instruments ............................................................................... 34 6.2.1 Listing Forms ............................................................................................................... 34 6.2.2 Questionnaires ............................................................................................................. 35 6.3.3 Instruction Manuals (Training Manual and Enumerators’ Manual) ............................ 35 6.4 Preparation of Tabulation Plan .................................................................................... 36 6.5 User-Producer Workshop ............................................................................................ 36 6.6 Pilot Test ..................................................................................................................... 36 6.7 Preparation of Information, Education and Communication (IEC) Materials ............ 37 6. 8 Census Logistics .......................................................................................................... 37 6.8.1 Procurement of Materials and Printing of Census Instruments and IEC Materials .... 38 6.8.2 Transportation and Distribution of the Census Instruments and Materials to the Regions .............................................................................................. 38 6.9 Training of Field Staff ................................................................................................. 38 6.10 The National Level Training – Training of Trainers (ToT)) ....................................... 39 6.11 The District level Training ........................................................................................... 39 CHAPTER 7 ..................................................................................................................................... 40 AGRICUTURAL SAMPLE CENSUS FIELD WORK ............................................................... 40 7.1 Introduction .................................................................................................................. 40 7.2 Field Organization ....................................................................................................... 40 7.3 Listing Exercise ........................................................................................................... 41 7.4 Enumeration Exercise .................................................................................................. 42 7.4.1 Smallholder enumeration ............................................................................................. 42 7.4.2 Enumeration of Large Scale Farms ............................................................................. 42 7.4.3 Community Level Enumeration .................................................................................. 42 iv 7.5 Collection and Reception of the Filled in Census Questionnaires from the Regions .. 43 CHAPTER 8 ..................................................................................................................................... 43 CENSUS DATA PROCESSING .................................................................................................... 43 8.1 Data Processing ........................................................................................................... 43 8.2 Manual Editing of the Completed Census Questionnaires .......................................... 43 8.3 Scanning and Data Capture ......................................................................................... 44 8.3.1 Scanning and Questionnaire Handling ........................................................................ 44 8.3.2 Optical Character Recognition .................................................................................... 45 8.3.3 Lessons learned from the Scanning/Extraction Process .............................................. 45 8.4 Design of a Data Structure Formatting Application .................................................... 46 8.5 Data Validation and Cleaning Exercise ....................................................................... 46 8.6 Tabulation of the Census Data ..................................................................................... 47 8.7 Application of Sampling Weights ............................................................................... 48 CHAPTER 9 ..................................................................................................................................... 49 RECOMMENDATIONS AND CONCLUSION ........................................................................... 49 9.1 Recommendations ........................................................................................................ 49 9.2 Conclusion ................................................................................................................... 49 v ACRONYMS ACLF Agriculture Census Listing Form ASDP ASLMs Agriculture Sector Development Programme Agricuture Sector Lead Ministries CSPro Census and Survey Processing System CSTWG Census and Surveys Technical Working group DANIDA Danish Development Agency DADIPS District Agricultural Development and Investment Projects DFID Department for International Development EA Enumeration Area EU European Union FAO Food and Agriulcultural Organization GDP GIS Gross Domestic Product Geographical Information System ICR Intelligent Character Recognision IEC Information, Education and Communication JICA Japan International Development Agency MAFC Ministry of Agriculture, Food Security and Cooperatives MALE MDAs Ministry of Agriculture, Livestock and Environment Ministries Departments and Agencies NACTE National Council for Technical Education NBS NGO Nationa Bureau of Statistics Non – Governmental Organization NMS OCGS NSGRP Nationa Master Sample Office of the Chief Government Statistician National Strategy for Growth and Reduction of Poverty OCR PMO-RALG Optical Character Recognition Prime Minister’s Office, Regional Administration and Local Government PPS Probability Proportional to Size PRS Poverty Reduction Strategy PSU Primary Sampling Unit REPOA Research on Poverty Alleviation RSM Regional Statistical Manager SPSS Statistical Package for Social Science TASAF Tanzania Social Action Funds ToT Training of Trainners UNDP United Nations Development Programme UNICEF United Nations Children Education Funds vi PREFACE At the end of the 2007/08 Agricultural Year, the National Bureau of Statistics (NBS) in collaboration with the Ministries of Agriculture, Food Security and Cooperatives, Livestock and Fisheries Development; Water; Industry and Trade; the Prime Minister’s Office, Regional Administration and Local Government (PMO-RALG) and the Office of the Chief Government Statistician, (OCGS), Ministries of Agriculture and Natural Resources; Livestock and Fisheries conducted the 2007/08 Agricultural Sample Census. This is the fourth Agricultural Census to be carried out in Tanzania, the first one was conducted in 1971/72, the second in 1993/94 and 1994/95 (during 1993/94 data on household characteristics and livestock count were collected and data on crop area and production in 1994/95), and the third was conducted in 2002/03. It is considered that this census is one of the largest to be carried out in Africa and indeed in many other countries of the world. The census collected detailed data on crop production, crop marketing, crop storage, livestock production, fish farming, and poverty indicators. In addition to this, the census was large in its scope and coverage as it provides data that can be disaggregated at district level and thus allow comparisons with the 2002/03 National Sample Census of Agriculture. The census covered smallholders in rural areas only and all the large scale farms. This report presents the technical and operational aspects of the census from planning to execution stage. It has also included in the annex all the instruments used in the whole operation. The extensive nature of the census in relation to its scope and coverage is a result of the increasing demand for more detailed information to assist in the proper planning of the agricultural sector and in the administrative decentralization of planning to district level. It is hoped that this report will provide new insights for planners, policy makers, researchers and others involved in the agricultural sector in order to improve the prevailing conditions faced by agricultural households in the country. Furthermore, the report will provide deeper understanding on the procedures and techniques applied in carrying out the census. On behalf of the Government of Tanzania, I wish to express my appreciation for the financial support provided by the development partners, in particular, the Department for International Development (DFID) and the Japanese Government through the Japan International Cooperation Agency (JICA) and others who contributed through the pool fund mechanism. My appreciation also goes to all those who in one-way or the other have contributed to the success of the census. In particular, I would also like to mention the enormous effort made by the Planning Group composed of professionals from the Agriculture Statistics Department of the National Bureau of Statistics, Ministry of Agriculture, Food Security and Cooperatives, Ministry of Livestock Development and Fisheries, Ministry of Water and Irrigation, Ministry of Agriculture, Livestock and Environment, Zanzibar, the Prime Minister's Office, Regional Administration and Local Government, Ministry of Industries, Trade and Marketing and the Office of the Chief Government Statistician, Zanzibar, the Food and Agriculture Organization of the United Nations and the Censuses and Surveys Technical Working Group (CSTWG). Finally, I would like to extend my sincere gratitude to all professional staff of the National Bureau of Statistics, the sector Ministries of Agriculture and PMO-RALG, the Consultants as well as Regional and District Supervisors and field enumerators for their commendable work. Certainly without their dedication, the census would not have been such a success. Dr. Albina Chuwa Director General National Bureau of Statistics 1 CHAPTER 1 GENERAL BACKGROUND 1.0 Background Information 1.1 Introduction Agriculture is an important sector of the Tanzanian economy in terms of food production, employment generation, production of raw material for industries, and generation of foreign exchange earnings. The agricultural sector produced about 26 percent of GDP (Economic Survey, 2008). Having a diversity of climatic and geographical zones, Tanzania’s farmers grow a wide variety of food and cash crops as well as fruits, vegetables and spices. Tanzania Mainland has approximately 50 million hectares of land suitable for grazing and is the third with largest livestock population in Africa after Sudan and Ethiopia. In 2007/08 the contribution of livestock to GDP was 4.7 percent and the contribution of livestock to the agriculral sector was estimated to be 8.9 percent. The main types of livestock raised in Tanzania are cattle, goats, sheep, pigs and chicken. Besides meat production, other products from livestock include hides and skins, milk and eggs. Livestock also contributes to crop and vegetable production by providing draft animals for cultivation and organic fertilizers. The Censuses of Agriculture provide a comprehensive and up-to-date picture of the situation in agriculture at the levels of administrative districts and regions as well as for the whole country. They are conducted only after every 5 years and depending on the availability of resources. The National Sample Census of Agriculture 2007/2008 was conducted with similar objectives and the reports have been produced in six volumes. This report (Volume 1) covers the technical and operational aspects of the census. Other Census reports include the Crop Report (Volume II), Livestock Report (Volume III), 21 Regional Reports for the Mainland (Volume IV), Large Scale Farms Report (Volume V) and a separate report for Zanzibar (Volume VI). Unlike in the 2002/03 Sample Census, the 2007/08 Sample Census report does not have separate reports on Household Characteristics and gender specific issues. Other thematic reports will be produced depending on the demand and availability of financial resources. 2 1.2 Rationale of the 2007/08 Agricultural Sample Census The Government of Tanzania has embarked on various plans geared to eradicate extreme poverty by the year 2025 and Tanzania Zanzibar by the year 2020. In order to facilitate intervention and monitoring activities of the Poverty Monitoring Master Plan, the government has planned a series of censuses and surveys to assist in policy formulation, planning and to track changes in the wellbeing of the population of Tanzania. In this Master Plan, a series of agricultural surveys have been planned, the first one was undertaken in 2002/03 agricultural year, the second for the year 2007/08, and the third for the year 2012/13. Demands for reliable and timely agricultural data have become significantly increasing for monitoring outcomes and progress of the poverty monitoring tools like the Agricultural Sector Development Programme (ASDP) and performance of the respective MDAs (ASLMs). Following the decentralization of the Government’s administration and planning functions, there has been a pressing need for agricultural and rural development data disaggregated at regional and district levels. The provision of district level estimates provide essential baseline information on the state of agriculture that supports decision making by the Local Government Authorities and in the design of District Agricultural Development and Investment Projects (DADIPS). The increase in investment is an essential element in the National Strategy for Growth and Reduction of Poverty (NSGRP). 1.2.1 Census objectives The 2007/08 Agricultural Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmers’ organizations, and others. The dataset is both more numerous in its sample and detailed in its scope and coverage so as to meet the user demand. The census was carried out in order to:  Identify any structural changes,in the size of farm household holdings, crop and livestock production, farm input and implement use. It also seeks to determine if there are any improvements in the rural infrastructure and the level of agricultural households living conditions; 3  Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Food Security and other stakeholders; and  Obtain data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing and service delivery. 1.2.2 Census Scope and Coverage The census was conducted for both large and small scale farms. The large scale farms were fully covered while the small scale farms were covered on a sample basis. The overall sample for small holders had a total of 3,509 villages/EAs consisting of 3,192 villages in Tanzania Mainland and 317 rural EAs in Tanzania Zanzibar. The data were therefore collected from a total sample of 52,635 rural agricultural households of which 48,880 were from the Mainland and 4,755 were from Zanzibar. A total of 1,006 large scale farms (968 on the Mainland and 38 in Zanzibar) were enumerated. The census used three different questionnaires:  Small scale farm questionnaire  Community level questionnaire  Large scale farm questionnaire The small scale farm questionnaire was the main census instrument and it included questions related to crop and livestock production and practices; population demographics; access to services, community resources and infrastructure; issues on poverty and gender. The main topics covered were:  Household demographics and activities of the household members  Land access, ownership, tenure and use  Crop and livestock production and productivity  Access to inputs and farming implements  Access and use of credit  Access to infrastructure (roads, district and regional headquarters, markets, advisory services, schools, hospitals).  Crop marketing, storage and agro processing  Tree farming, agro-forestry, and fish farming  Access and use of communal resources (grazing land, communal forests, water for humans and livestock, beekeeping) 4  Investment activities ( irrigation structures, water harvesting, erosion control, fencing)  Off farm income and non agricultural related activities  Households living conditions (housing, sanitary facilities )  Livelihood constraints  Poverty Indicators The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate prices. The Large Scale Farm questionnaire was administered to large farms either privately or corporately managed. 1.3 Main Activities Undertaken The main focus at all stages of the census operation was on data quality which was strongly emphasized all the time. The main activities undertaken include:  Census organization  Tabulation plan preparation  Sample design  Design of census questionnaires and other instruments  Pilot-test  Training of trainers, supervisors and enumerators  Information Education and Communication (IEC) campaign  Data collection  Field supervision and consistency checks  Data processing: o Scanning o Structure formatting application o Batch validation application o Manual data entry application o Tabulation preparation using SPSS and Excel  Table formatting and charts using Excel, map generation using Arc GIS and Excel  Report preparation using Word and Excel 5 CHAPTER 2 METHODOLOGY 2.1 Census Organization The Census was conducted by the National Bureau of Statistics (NBS) in collaboration with the Ministries of Agriculture, Food Security and Cooperatives, Livestock and Fisheries Development; Water; Industry and Trade; and the Prime Minister’s Office, Regional Administration and Local Government in Tanzania Mainland. For Tanzania Zanzibar, it was the Office of the Chief Government Statistician (OCGS), Ministries of Agriculture and Natural Resources; Livestock and Fisheries. At the National level, the Census was headed by the Director General of the National Bureau of Statistics, Tanzania Mainland in collaboration with the Chief Government Statistician,Tanzania Zanzibar. The Planning Group formed by the Director General of NBS and the Chief Government Statistician consisted of staff from the Department of Agricultural Statistics of NBS, Department of Economic Statistics of OCGS, Department of Policy and Planning of the Ministry of Agriculture, Food Security and Cooperatives, Department of Policy and Planning of the Ministry of Livestock and Fisheries Development in in the Mainland, the Ministry of Livestock and Fisheries and the Ministry of Agriculture and Natural Resources in Zanzibar. The Planning Group was responsible for all the census operations. For Tanzania Mainland, implementation of census activities at the regional level was overseen by the Regional Statistical Managers of NBS and the Regional Agricultural Officers from the Prime Minister’s Office, Regional Administration and Local Government. At the district level, each district was managed by two supervisors from the Prime Minister’s Office, Regional Administration and Local Government (PMO-RALG). All the enumerators were from the PMO-RALG. As for Tanzania Zanzibar, the implementation of the census activities at regional level was overseen by the Regional Statistical Officers and Regional Agricultural Officers. At district level, implementation of the census activities were managed by District Agricultural Development Officers (DADOs) while at National level, there was a national mobile team to supervise the census operations. The Censuses and Surveys Technical Working Group (CSTWG) under MKUKUTA provided support in sourcing financing, approving budget allocations and monitoring progress of the census. 6 A Technical Committee for the census was established with members from key stakeholder organisations. Its main function was to approve the proposed instruments and procedures developed by the Planning Group. It also approved the tabulations and analytical reports prepared from the census data. 2.2 Tabulation Plan Preparation The tabulation plan was developed considering the tabulations from previous censuses and surveys to allow trend analysis and comparisons as well as the needs of end users. 2.3 Sample Design The Mainland sample consisted of 3,192 villages. The villages were drawn from the National Master Sample (NMS) developed by the National Bureau of Statistics (NBS) to serve as a national framework for the conduct of household based surveys in the country. The National Master Sample was developed from the previous 2002 Population and Housing Census. The total Mainland sample was 47,880 agricultural households while in Zanzibar, a total of 317 EAs were selected and 4,755 agricultural households were covered. More details on the sampling are as shown on Chapter Three. Table 1: Census SamplIn both Mainland and Zanzibar, a two stage sampling was used. The numbers of villages/Enumeration Areas (EAs) were selected for the first stage with a probability proportional to the number of villages/EAs in each district. In the second stage, 15 households were selected from a list of agricultural households in each village/EA using systematic random sampling. Table 1 gives the sample size of households, villages/EAs and districts for Tanzania Mainland and Tanzania Zanzibar. 2.4 Questionnaire Design and Other Census Instruments The questionnaires were designed following users demand to ensure that the questions asked were in line with the users data needs. Several features were incorporated into the design of the questionnaires so as to increase the accuracy of the data: Number Mainland Zanzibar Total Households 447,880 4,755 52,635 Villages/EAs 3,192 317 3509 Districts 133 9 142 Regions 21 5 26 7  Where feasible, all variables were extensively coded to reduce post enumeration coding errors  The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions while interviewing the respondent  The responses to all questions were placed in boxes printed on the questionnaire, with one box per character.  This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data capture.  Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent Each section was clearly numbered, which facilitated the use of skip patterns and provided a reference for data type coding for the programming of CSpro and SPSS. Three other instruments were used:  Village Listing Forms were used for the listing of households in the villages/EAs and from this list, a systematic sample of 15 agricultural households were selected.  A Training Manual which was used by the trainers for the cascade/pyramid training of supervisors and enumerators  Enumerator Instructions Manual which was used as reference material. 2.5 Field Pilot-Testing The Questionnaire was pilot-tested in four locations (Arusha, and Dodoma, on the Mainland and Unguja and Pemba in Zanzibar). This was done to test the wording, flow and relevance of the questions and to finalise crop lists, questionnaire coding and manuals. In addition, several data collection methodologies had to be finalized, namely, livestock numbers in pastoralist communities, mixed cropping, use of percentages in the questionnaire and finalization of skip patterns and documentation of consistency checks. 8 2.6 Training of Trainers, Supervisors and Enumerators During the training, cascade/pyramid training techniques were employed to maintain statistical standards. The top level training was provided to 78 national and regional supervisors (65 from Mainland and 13 from Zanzibar). The trainers were members of the Planning Group from the National Bureau of Statistics, the sector Ministries of Agriculture and the Office of the Chief Government Statistician, Zanzibar. In each region, three training sessions were conducted for the district supervisors and enumerators. The training concentrated more on questionnaires, listing forms, field level census methodology, and definitions. Emphasis was placed on consistency checking in the field. Tests were eventually given to the trainees (supervisors and enumerators) and the best 50 percent of the trainees were selected for the enumeration using the smallholder questionnaire and the community level questionnaire. 2.7 Information, Education and Communication (IEC) Campaign Radios, televisions, newspapers, leaflets, t-shirts and caps were used to create awareness among the public on the Agricultural Sample Census. This helped in sensitizing the public on field level activities in order to increase the response rate. The t-shirts and caps were given to the field staff and village chairpersons. The village chairpersons assisted the enumerators in locating the selected households. 2.8 Data Collection Data collection activities for the 2007/08 Agricultural Sample Census lasted for three months from June to August, 2009. The interview method was used to collect data during the census. Data collection was monitored by a hierarchical system of supervisors which included the Mobile Response Team, Regional and District Supervisors. The Mobile Response Team, which was headed by the Manager of Agriculture Statistics Department, provided an overall direction to the field operations and responded to queries arising outside the scope of the training exercise. Decisions made on the definitions and procedures were then communicated back to the enumerators via the Regional and District Supervisors. On the Mainland, each region had two Regional Supervisors (total 42) and two district supervisors per district (total 266). District enumeration and supervision were performed by staff from the Prime Minister’s Office, Regional Administration and Local Government (PMO-RALG). Regional and national supervision was provided by senior staff from the National Bureau of Statistics and the sector Ministries of Agriculture. In Zanzibar, the enumeration was conducted by staff from the Ministry of Agriculture and 9 Natural Resources and Ministry of Livestock and Fisheries. Supervision was provided by senior officers of the same Ministries and the Office of the Chief Government Statistician. During the household listing exercise, 3,192 extension staff participated on the Mainland and a total of 177 enumerators participated during the listing exercise and the enumeration of small scale farms in Zanzibar. A total of 1,596 enumerators were involved in data collection of small scale farms on the Mainland. Additional five percent of the enumerators were kept as reserves in case of drop outs during the enumeration exercise. 2.9 Field Supervision and Consistency Checks Enumerators were trained on how to probe the respondents until they were satisfied with the responses given before they recorded them in the questionnaires. The first checks on the questionnaires were carried out by enumerators in the field during enumeration, followed by district, regional and national supervisors. Supervisory visits at all levels of supervision focused on the completeness of the questionnaires and data consistency. Any inconsistencies encountered were corrected, and where necessary, call backs to the respective respondents were made by the enumerators to obtain the correct information. Furthermore, quality control checks were made by the supervisors in each district. 2.10 Data Processing and Analysis Data processing involved the following processes:  Data entry  Data structure formatting  Batch validation  Tabulation 2.10.1 Data entry Scanning and ICR data capture technology for the smallholder questionnaire was used on the Mainland. This did not only increase the speed of data entry, it also increased the accuracy due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to trap errors during the verification process. The scanning operation was so successful that it is highly recommended that the technology be adopted for future censuses and surveys. 10 Prior to scanning, all questionnaires underwent a manual cleaning exercise. This involved checking that the questionnaire had a full set of pages, correct identification, clean and good hand writing. Questionnaires found dirty or damaged and generally unsuitable for scanning were put aside for manual data entry. CSPro was used for data entry of all Large Scale Farms and Community based questionnaires due to the relatively small number of questionnaires. It was also used to enter smallholder questionnaires that were rejected by the ICR extraction application as well as those found unsuitable for scanning during the manual editing exercise. 2.10.2 Batch validation A batch validation program was developed in CSPro in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to more complexes checking between variables. It took six months to screen, edit and validate the data from the smallholder questionnaire. After the long process of data cleaning, the tabulations were prepared based on the pre- designed tabulation plan. 2.10.3 Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while Arc GIS (Geographical Information System) was used in producing the maps. 2.10.4 Analysis and Report Preparation The report writing was outsourced to Sokoine University of Agriculture, the analysis in the reports focused on regional comparisons, time series and national production estimates. Microsoft Excel was used to produce charts; Arc GIS and Excel were used to generate maps, whereas Microsoft Word was used in compiling and writing up the reports. 2.11 Data Quality Control A great deal of emphasis was placed on data quality throughout the whole exercise from planning, questionnaire design, training, supervision, data entry, validation and cleaning/editing. As a result of 11 this process, it is believed that the census is highly accurate and representative of what was experienced at the field during the census operation. With very few exceptions, the variables in the questionnaires were within the norms for Tanzania and they followed the expected time series trends when compared to historical data. 2.12 Funding Arrangements The 2007/08 Agricultural Sample Census was supported mainly by the Department for International Development (DFID) and the Japan International Cooperation Agency (JICA) who financed most of the operational activities. Other funds were from the Government of Tanzania. In addition, technical assistance was provided by the Food and Agriculture Organization (FAO). 12 CHAPTER 3 CENSUS ORGANIZATION 3.1 General Overview For any project to succeed, careful planning, monitoring and evaluation of all activities is essential. However, so as to be able to plan, monitor and evaluate a project properly, a good organizational structure is very vital. This was also the case with the 2007/08 Agricultural Sample Census. The Government Implementing Agency for the 2007/08 Agricultural Sample Census was the National Bureau of Statistics (NBS), Tanzania Mainland in collaboration with the Office of the Chief Government Statistician (OCGS), Tanzania Zanzibar. These offices were responsible for the census accounts and closely coordinated and monitored the implementation of the census by providing proper collaboration with the Ministry of Agriculture, Food Security and Cooperatives; the Ministry of Water, Ministry of Livestock and Fisheries Development; the Ministry of Industry and Trade; and the Prime Minister’s Office, Regional Administration and Local Government, the Ministry of Agriculture and Natural Resource and the Ministry of Livestock and Fisheries . 3.2 Census Administration 3.2.1 National level At national level, there was a planning group, responsible for the overall planning of the census activities including:  Determining the scope and coverage of the census;  Designing and testing of the sample census instruments;  Preparing the tabulation plan;  Preparing editing specifications;  Preparing Information, Education and Communication (IEC) materials;  Ensuring timely availability of necessary materials for the census;  Ensuring timely transportation of census materials to the regions;  Ensuring recruitment of suitable and qualified enumerators and supervisors;  Ensuring that supervisors and enumerators are properly trained;  Supervision of the listing and enumeration exercises; 13  Ensuring that all the questionnaires from the regions are received on time, filed and stored properly; and  Supervising the editing, scanning and verification exercises., In addition, the planning group was responsible for conducting the Training of Trainers (To;T). The trainers were then responsible for training the enumerators and supervisors. The national level trainees were the ones who trained the enumerators and supervisors. 3.2.2 Regional level At regional level, there were three supervisors involved in administering census activities. These were: Headquarters supervisor, Regional Statistical Manager (RSM) and the Regional Agricultural Advisor (RAA). Their main responsibility was to closely monitor census activities at the regional level including the following:  Ensuring that listing and enumeration materials for the respective regions are in place on time;  Monitoring and supervising the listing, enumeration and post enumeration checks ;  Disbursing funds at the district level; and  Ensuring safe and timely transportation of census questionnaires back to NBS Headquarters. 3.2.3 District level At district level, there were two district supervisors who were responsible for:  Ensuring that census documents and other materials were received as planned;  Ensuring that IEC materials were distributed to the target groups;  Monitoring implementation of technical issues during the field work;  Field editing of the filled in questionnaires; and  Overall supervision of the census in the respective districts. 3.3 Composition and Functions of the Technical Committee 3.3.1 Agricultural Sample Census Technical Committee At the national level, there was a Census Technical Committee that was formed by the Director General of the National Bureau of Statistics and the Chief Government Statistician of the OCGS. The Committee comprised statisticians, agricultural economists, agronomists and livestock officers from 14 the National Bureau of Statistics (NBS), Agriculture Sector Lead Ministries (ASLMs), Tanzania Food and Nutrition Centre, University of Dar es Salaam, and the President’s Office, Planning and Privatization. . The functions of the Technical Committee included:  Providing advice on the content of the questionnaires and other census documents;  Ensuring that appropriate methodologies, concepts and definitions were adopted;  Advising the planning group on how to handle some of the technical issues;  Providing advice on the recruitment of enumerators and supervisors; and  Ensuring that the census activities were carried out as planned. 3.3.2 Censuses and Surveys Technical Working Group The Censuses and Surveys Technical Working Group (CSTWG) is one of the three technical working groups that coordinate and monitor activities of the National Poverty Monitoring System. It was established by the Government in 2001 to track and evaluate progress through the poverty monitoring indicators. The National Strategy for Growth and Reduction of Poverty and Zanzibar Strategies for Growth and Reduction of Poverty (ZSGRP) identified agriculture as one of the key sectors in the poverty reduction strategy. One of the objectives of the 2007/08 Agricultural Sample Census was to monitor performance of the poverty indicators. The Censuses and Surveys Technical Working Group closely monitored the Agricultural Sample Census to ensure that it was successfully conducted and that the available resources were utilized properly. The members of the Censuses and Surveys Technical Working Group were from various institutions and donor agencies including UNICEF; DFID; EU; UNDP; JICA; Vice President’s Office; the Ministry of Community Development; Gender and Children; the Ministry of Labour; the Ministry of Health; Tanzania Social Action Fund (TASAF); Ministry of Agriculture, Food Security and Cooperatives; the Ministry of Water, and the Ministry of Livestock and Fisheries Development; the Prime Minister’s Office, Regional Administration and Local Government; the University of Dar es Salaam; the National Bureau of Statistics and the Office of the Chief Government Statistician. 15 3.3.3 Information, Education and Communication (IEC) and Advocacy Information, Education and Communication (IEC) was an important aspect of the census. The advocacy was undertaken to make the public and stakeholders fully aware of the importance of the census and the data to be generated from the exercise. The main objectives of the IEC were:  To sensitize and mobilize the public so that they would support, cooperate and participate fully in the Agricultural Sample Census; and  To promote acceptance and extensive use of the Agricultural Sample Census data. The public was well informed on the content of the questionnaires, uses of the census data in relation to development planning and agricultural policy formulation as well as the roles played by stakeholders during the census. The methods applied in educating the public were tailored to suit specific needs of the census target groups. Radios and newspaper messages were used to educate the public particularly in the rural areas where televisions are hardly available. The t-shirts were for the staff involved in the census field work. The leaflets were used to sensitize and educate the targeted groups in the selected villages. 3.4 Census Logistics The 2007/08 Agricultural Sample Census was a big project that required purchasing of a number of materials, printing of various documents and transportation of the materials to and from the districts and regions. The formation of a logistics team was one way of ensuring that the mentioned activities would be done efficiently and timely. 3.4.1 Logistics team The logistics team was established in order to ensure smooth and efficient handling and transportation of census materials. The team members comprised the census Desk Officer, one agricultural economist, one supplies officer, one office supervisor and one administrative officer. The team was responsible for the following tasks:  Acquisition of adequate supplies, safe and timely delivery of all census materials and equipment; 16  Facilitating printing of all census documents;  Transporting materials from Dar es Salaam to the regions and districts;  Providing backup support to the field teams during training, listing and enumeration periods; and  Ensuring availability of supervision vehicles in the regions. One vehicle was assigned in each region for the supervision. 17 CHAPTER 4 SAMPLE DESIGN FOR THE CENSUS 4.1 Introduction Viable development of the agricultural sector needs proper policy formulation, efficient planning and implementation. These aspects call for both accurate and reliable statistical information whether collected through sample surveys or censuses. The Government of Tanzania has embarked on various plans geared at eradicating extreme poverty by the year 2025. This initiative enhances the need for timely and accurate statistical information to facilitate planning and action towards poverty eradication. Among the various activities embarked by the Government for the Poverty Monitoring Master Plan was the conduct of an agricultural sample census in the year 2007/08. The sample census was expected to provide poverty tracking indicators for use in monitoring the success of the project. For a country like Tanzania whereby development planning is decentralized, it is important to have statistical information down to the district level where most of the planning normally takes place. As such, the sample design for the 2007/08 Agricultural Sample Census was developed to provide district level estimates so as to facilitate planning at that level. 4.2 Design of the National Master Sample The former Central Bureau of Statistics developed the first National Master Sample (NMS) in 1986. The sample was developed as a national framework for integrating and systematizing the conduct of household based surveys done by various ministries and institutions in the country. Given the long period that has elapsed since the first NMS was developed, it was found necessary to revise the NMS basing on the 2002 Population and Housing Census. The revised NMS, which was used in the 2002/03 Agricultural Sample Census was also used in 2007/08 to facilitate the sample design for the 2007/08 Agricultural Sample Census. 4.3 Design of the National Sample Census of Agriculture Due to scarcity of resources, it was decided that the 2007/08 Agricultural Sample Census be conducted on a sample basis for small agricultural holders and a census for all large scale farms. The sample 18 design for the small holders was expected to give estimates down to the district level for the rural part of both Tanzania Mainland and Tanzania Zanzibar. However, in determining the respective sample size, consideration was made on the available resources, the need to ensure manageability of the sample, minimization of costs and the level of planning. The 2007/08 Agricultural Sample Census covered both the Mainland and Zanzibar. The sample that was used was the rural part of module B sample of the National Master Sample for Tanzania.This sample gives estimates down to the district level. The sample design was a stratified two-stage sample, where the rural part of Tanzania was stratified into districts. The first stage (Primary Stage) units were villages in the case of Tanzania Mainland and rural enumeration areas in the case of Tanzania Zanzibar. In the first stage, villages/EAs were selected in each rural part of the district. About 27 villages/rural EAs per district were selected. The sample was expected to give estimates of different parameters with error margin of 5 percent at 95 confidence level. The selection of villages/rural EAs was as follows:  All villages/rural EAs were selected if the number of villages/rural EAs in the district was less than or equal to 27;  27 villages/rural EAs were selected if the number of villages/rural EAs in the district was greater than 27; and  All villages covered during the 2002/03 census were considered. Additional villages were sampled in the new districts to reach the required number of 27 sampled villages. In the second stage, farming households were selected in each of the selected villages/EAs. A sample of 15 farming households was selected per selected village/rural EA..The villages/EAs in the first stage were selected with probability proportional to the number of households in the village (PPS). The cumulative total method was used to achieve the PPS selection of villages/EAs. In the second stage (Secondary Stage), farming households were selected using a systematic random sampling procedure whereby a list of farming households was compiled from each selected village/EA and a systematic random sample was then drawn. 19 Table 2 shows the number of selected clusters (villages or rural EAs) per district and region in both Tanzania Mainland and Tanzania Zanzibar. Table 2: List of Regions, Districts, Number of villages/rural EAs selected in the 2002/03 and 2007/08 Agricultural Censuses Region District Number of villages/rural EAs Number of villages/rural EAs in 2002/03 Census Number of villages/rural EAs in 2007/08 Census 01 Dodoma 01 Kondoa 02 Mpwapwa 03 Kongwa 05 Dodoma Urban 06 Bahi 07 Chamwino 167 88 66 48 56 72 40 27 27 27 14 16 27 27 27 27 27 27 02 Arusha 01 Monduli 03 Arusha Urban 04 Karatu 05 Ngorongoro 06 Longido 07 Arusha Rural 08 Meru 40 4 46 41 32 76 69 15 4 27 27 12 13 15 27 4 27 27 27 27 27 03 Kilimanjaro 01 Rombo 02 Mwanga 03 Same 04 Moshi Rural 05 Hai 06 Moshi Urban 07 Siha 63 52 73 150 66 0 30 27 27 27 40 18 0 5 27 27 27 27 27 0 23 04 Tanga 01 Lushoto 02 Korogwe 03 Muheza 04 Tanga 05 Pangani 06 Handeni 07 Kilindi 08 Mkinga 155 132 100 40 32 108 64 75 40 30 21 27 27 27 27 14 27 27 27 24 24 27 27 27 05 Morogoro 01 Kilosa 02 Morogoro 03 Kilombero 04 Ulanga 05 Morogoro Urban 06 Mvomero 155 133 73 63 25 97 40 30 27 27 25 27 27 27 27 27 25 27 06 Pwani 01 Bagamoyo 02 Kibaha 03 Kisarawe 04 Mkuranga 05 Rufiji 06 Mafia 80 55 75 101 90 20 27 27 27 30 27 20 27 27 27 27 27 20 07 Dar Es Salaam 01 Kinondoni 02 Ilala 03 Temeke 20 10 23 20 10 23 20 10 23 08 Lindi 01 Kilwa 90 27 27 20 Region District Number of villages/rural EAs Number of villages/rural EAs in 2002/03 Census Number of villages/rural EAs in 2007/08 Census 02 Lindi Rural 03 Nachingwea 04 Liwale 05 Ruangwa 06 Lindi Urban 117 85 38 71 6 30 27 27 27 6 27 27 27 27 6 09 Mtwara 01 Mtwara Rural 02 Newala 03 Masasi 04 Tandahimba 05 Mtwara Urban 06 Nanyumbu 108 118 149 108 6 79 30 30 23 30 6 13 27 27 27 27 6 27 10 Ruvuma 01 Tunduru 02 Songea Rural 03 Mbinga 04 Songea Urban 05 Namtumbo 109 63 182 17 64 30 27 40 17 27 27 27 27 17 25 11 Iringa 01 Iringa Rural 02 Mufindi 03 Makete 04 Njombe 05 Ludewa 07 Iringa Urban 08 Kilolo 09 Njombe Mjini 112 125 93 166 64 6 75 43 30 30 27 15 27 6 27 9 27 27 27 27 27 6 27 27 12 Mbeya 01 Chunya 02 Mbeya (R) 03 Kyela 04 Rungwe 05 Ileje 06 Mbozi 07 Mbarali 08 Mbeya Urban 73 126 83 155 68 167 83 24 27 30 27 40 27 40 27 24 27 27 27 27 27 27 27 24 13 Singida 01 Iramba 02 Singida Rural 03 Manyoni 04 Singida Urban 120 143 72 18 30 40 27 18 27 27 27 18 14 Tabora 01 Nzega 02 Igunga 03 Uyui 04 Urambo 05 Sikonge 06 Tabora Urban 131 95 93 110 46 27 30 27 27 30 27 27 27 27 27 27 27 27 15 Rukwa 01 Mpanda 02 Mpanda Mjini 03 Sumbawanga 04 Nkasi 05 Sumbawanga Urban 75 0 168 84 32 18 0 40 27 27 27 0 27 27 27 16 Kigoma 01 Kibondo 02 Kasulu 03 Kigoma Rural 04 Kigoma Urban 68 92 80 5 27 27 27 5 27 27 27 5 17 Shinyanga 01 Bariadi 137 30 27 21 Region District Number of villages/rural EAs Number of villages/rural EAs in 2002/03 Census Number of villages/rural EAs in 2007/08 Census 02 Maswa 03 Shinyanga Rural 04 Kahama 05 Bukombe 06 Meatu 07 Shinyanga Urban 08 Kishapu 78 108 204 126 70 23 101 27 30 40 30 27 23 30 27 27 27 27 27 23 27 18 Kagera 01 Karagwe 02 Bukoba 03 Muleba 04 Biharamulo 05 Ngara 07 Missenyi 08 Chato 06 Bukoba Urban 119 92 118 46 74 74 71 8 30 27 30 27 27 27 27 8 27 27 27 27 27 27 27 8 19 Mwanza 01 Ukerewe 02 Magu 03 Kwimba 04 Misungwi 05 Sengerema 06 Geita 07 Ilemela 08 Nyamagana 68 123 107 78 122 185 18 0 27 30 30 27 30 40 18 0 27 27 27 27 27 27 17 0 20 Mara 01 Tarime 02 Serengeti 03 Musoma Rural 04 Bunda 05 Musoma Urban 06 Rorya 77 71 105 85 3 80 19 27 30 27 3 15 27 27 27 27 3 27 21 Manyara 01 Simanjiro 02 Kiteto 03 Babati 04 Hanang 05 Mbulu 32 44 81 54 68 27 27 27 27 27 27 27 27 27 27 Total Mainland 10147 3217 3192 51 Kaskazini – Unguja 01 North A 02 North B 194 112 40 30 40 30 52 Kusini – Unguja 01 Central 02 South 134 58 30 27 30 27 53 Mjini Magharibi 01 West 02 Urban 183 0 40 0 40 0 54 Kaskazini – Pemba 01 Wete 02 Micheweni 159 148 40 40 40 40 55 Kusini – Pemba 01 Chakechake 02 Mkoani 130 157 30 40 30 40 Total Zanzibar 1275 317 317 Total Tanzania 11422 3534 3,509 22 The overall sample for small holders in the 2007/08 Agricultural Sample Census had a total of 3,509 villages/rural EAs consisting of 3,192 villages in Tanzania Mainland and 317 rural EAs in Tanzania Zanzibar. Appendix 1 shows the list of selected villages/rural EAs per region, district and ward/shehia. 4.4 Basic Formulae for Estimation In the sample, the primary stage unit was the village/rural EA. The estimates obtained were for the village/rural EA, rural part of the District, rural part of the Region and rural part of the Nation. The selection of the villages/rural EAs was with probability proportional to the number of households in the village/rural EA and systematic random selection procedure was used to select households. Let ykij be the observation on variable Y for household j in village/rural EA i of district k. 4.5 EA Estimates: (a) Estimate of total for i-th village/EA in the k-th District    i m 1 j kij ki ki y m M ˆ ki Y Where Mki = Number of households in the i-th village/rural EA in the k-th District. mki = Number of sampled households in the i-th village/rural EA in the k-th district. (b) Estimate of average for the i-th village/EA in the k-th District    ki m 1 j kij ki ki y m 1 Yˆ 4.6 District Estimates – Rural (a) Estimate of total    k n 1 i ki k / Yˆ M ˆ ki k k M n Y =     ki k m j kij n i k k k y m n M 1 1 1 23 Where Mk = Total Number of rural households in k-th district in the Year under study Mki = Total Number of households in the i-th village/EA in k-th District nk = Number of sampled villages/rural EAs in the k-th district. (b) Estimate of average k k k N Y Y ˆ ˆ  Where Nk= Total number of villages/rural EAs in the k-th district. 4.7 Regional Estimates Estimate of total The estimate of the regional total was obtained by summing up the estimates of the district totals in a given region. This was obtained using the following expression:    R D k k R Y Y 1 ˆ ˆ where R is the r-th region and R D is number of districts in region R. 4.8 National Estimates Estimate of the total The estimate of the national total for the rural part of the nation was obtained by either summing up estimate of district totals or estimate of regional totals. This was obtained using the following expression:       R R R D k k r Y Y Y 1 1 ˆ ˆ ˆ where D is the total number of districts in the nation and R the total number of regions in the nation. 4.9 Adjustment Since the estimated total population for the rural domain of study was not expected to be exactly equal to the projected total population for the year under study, the adjustment had to be done. The adjustment factor was multiplied to the village/rural EA weights so as to yield estimates that were close to the projected population values. 24 4.10 Adjustment Factor for the Rural Sample Adjustment Factor = Adj (r) = ) ( ) ( ˆ r r proj Y Z where ) (r proj Z is the projected rural population for the year under study and ) (ˆ r Y is the estimated rural population from the sample which is obtained as:         k n 1 i ki k 1 1 k ) ( / Yˆ M Yˆ ˆ ki k D k D k r M n Y =       ki k m j kij n i ki k k D k y m n M 1 1 1 1 where kij y is the number of people in the j-th household of the i-th sampled village/rural EA in the k-th District and D total number of districts in the country. So, the overall weight for the i-th village/rural EA in the k district was obtained from Adj(r) x ki k k ki m n M w  . Appendix II gives the weights as obtained following the above procedure. 25 CHAPTER 5 GENERAL TERMS, CONCEPTS AND DEFINITIONS 5.1 Important Considerations The main concepts and definitions that were used in the 2007/08 Agricultural Sample Census were familiar as they were also used in earlier agricultural censuses and surveys. However, some of them were mostly used in other statistical fields. 5.2. Concepts and Definitions It is important to mention that, the concepts and definitions defined here are those which are commonly used in censuses and surveys. 5.2.1 Household and Holding Household A household is a socio-economic unit that consists of one or more persons with common living and catering arrangements. Such persons are usually not always related to each other by blood or by marriage. A one person household is a household where a person lives alone in a whole or part of a housing unit and has independent consumption. Multi-person household is a household where a group of two or more persons occupy the whole or part of a housing unit and share expenses. Usually, households of this type contain a husband, wife and children. Other relatives, boarders, visitors and other persons are included as members of the household if they pool their resources and share their consumption. Head of Household Head of household is a person who is acknowledged by all other members of the household to be the head either by virtue of his age or standing in the household. Holder A holder is a person who exercises management control over the agricultural holding operation and who takes major decisions regarding resource utilization or disbursement. 26 Agricultural Household (Farming Household) An Agricultural household is a household where one or more persons are holder(s). In peasant farming, there is normally a one-to-one correspondence between the agricultural household and the holding. 5.2.2 Holding Characteristics Agricultural Holding Agricultural Holding refers to an economic unit of agricultural production under single management. It consists of all livestock kept and all land used for agricultural production without regard to title. For the purpose of the 2007/08 Agricultural Sample Census, agricultural holdings were restricted to those that meet one or more of the following conditions:  Having or operating at least 25 square metres of arable land; and  Own or keep at least one head of cattle or five goats/sheep/pigs or fifty chicken/ducks/turkeys during the agricultural year 2007/08. Field This is a continuous piece of land cultivated as one by holder even if planted with different crops. An individual holding may consist of one or more such fields. Actual Area Planted This refers to the total area in acres or hectares that the household was able to plant. Harvested Area This refers to the total area in acres or hectares that the household get most of its production from. This is equal to the area planted less the area that was not harvested due to pests, wild animals, drought and the like. 5.3. Land access/ownership/tenure Land tenure refers to arrangements or rights under which the holder holds or uses land. A holding may be operated under one or more tenure forms. 27 (a) Area owned This refers to the land for which the holder possesses title of ownership and has the right to determine the nature and extent of its use. It excludes the area owned but rented to others. (b) Area under Customary Law This refers to the land which the household does not have an official title deed, but its right of use is granted by the traditional leaders. The right – user agreement does not have to be granted directly by the village leaders as right of access may be passed on through heredity. (c) Area Bought from others This refers to the area of customary land that has been bought from others. This land does not have a title deed and therefore is not leasehold. (d) Area Rented from others This refers to the land area rented or leased by the holder form other persons for a limited time period. It includes land rented for an agreed sum of money or a share of produce or land rented in exchange for services and land operated under other rental arrangements such as area granted rent-free. (e) Area Borrowed from others This refers to the land areas whereby its use is granted by the owner free of charge. The land owner can either be a lease holder or have the right of access through customary law. (f) Area share – cropped from others This refers to the system whereby use of land is granted on condition that the owner is given a certain percentage of the production realized from that piece of land. 28 5.3.1 Land Use Temporary Crops These are crops that are sown and harvested during the same agricultural year. Permanent Crops These are crops that normally take over a year to mature and once they mature they can be harvested for a number of years such as bananas, coffee, etc. Pure Stand Refers to a single crop cultivated in a field or plot at any one time. Mixed Crops This is a mixture of two or more crops planted together and mixed in the same plot or field. The crops can either be randomly planted together or they can be planted in a particular pattern. Pasture Land This is an area owned or set aside for livestock grazing. It can be an improved pasture where the farmer has planted grass, applied fertilizer or applied other production increasing technology to improve the grazing. Fallow This is the area of land that is normally used for crop production, but is not used for crop production during a year or a number of years. Normally, this is to allow for self-generation of fertility/soil structure and is often an integral part of crop rotation system. Natural Bush This refers to land which is considered productive but is not under cultivation or used extensively for livestock production and has naturally growing shrubs and trees. Planted Trees This refers to land, which is used for planting trees for poles and timber. 29 Unusable This refers to land that is known to be non – productive for agricultural purposes Agricultural Production Agricultural production refers to the growing and harvesting of different types of crops. It also includes keeping of livestock and poultry as well as production of livestock products. Agricultural Year Agricultural Year is a twelve-month cycle in which production of annual crops takes place. The Agricultural Year in Tanzania commences on the 1st of October and ends on the 30th of September of the following year. 5.4 Livestock This refers to all animals and fowls kept in the households (irrespective of ownership) and large – scale as well as their production. Indigenous Livestock These are livestock commonly reared in the villages, without special attention. Such livestock are given neither special feed nor special housing, and the like. They are not reared specifically for producing meat and milk. Improved Livestock Improved Livestock refers to livestock, which are bred specifically for producing meat and milk. These may be crossbred or pure bred. Oxen These are castrated male cattle over one year and are used specifically for doing farm work. They are also often fattened to produce quality beef. Cows These are mature female cattle that have given birth at least once. 30 Bulls These are mature un-castrated male cattle used for breeding Steers These are castrated male cattle over one year usually raised to produce beef. Heifers These are female cattle of one year up to the first calving. Calves These are young cattle under one year of age Billy Goat This refers to a mature un-castrated male goat used for breeding Kid This refers to a young goat less than nine months of age. Ram This refers to a mature un-castrated male sheep used for breeding Ewe This refers to a mature female sheep over nine months of age Boar This refers to a mature un-castrated male pig used for breeding Sow This is a mature pig that has given birth at least one litter of piglets Gilt This is a mature female pig of nine months up to the first furrowing 31 Piglet This refers to a young pig less than three months of age 5.4.1 Poultry These are fowls commonly kept in households or large Scale Farms (e.g. chicken, chicks, guinea fowls, etc). Indigenous Poultry This refers to fowls commonly kept in the village, without special attention. Such poultry are neither fed by special food nor special housing. Improved Poultry These are fowls commonly kept in households or Large Scale Farms (e.g. chicken, ducks, turkeys, guinea fowls, etc.) specifically for producing meat and eggs. 5.5 Irrigation Irrigation is the artificial application of water to the soil for the purpose of supplying the moisture essential for plant growth. Irrigation water is supplied to supplement the water available from rain. 5.5.1 Drainage This is the removal of excess water from land surface and/or the upper and layer to make the non- productive wetland productive. 5.5.2 Plot Plot is a portion of a field planted with one specific crop, for example, maize or sorghum or a crop mixture such as maize/beans mixture. 5.6 Fertilizers and Pesticides These are inputs, which are added to the soil or applied to the plants to increase nutrients to the soil and control and eliminate crop diseases. 32 5.6.1 Fertilizers These are mineral or organic substances, natural or manufactured, which are applied to soil, irrigation water or hydroponics medium, to supply plants with the necessary nutrients. The nutrients include, mineral fertilizers, organic source, manure, and composite (a) Mineral fertilizer Manufactured, usually through an industrial process (b) Organic Sources Materials of organic origin, either natural or processed, used as sources of plant nutrients. (c) Manure Refers to farm yard or animal manure, which is a mixture of solid excreta of animals with litter used for the bedding. (d) Composite Composite consists of organic materials of animal, plant or human origin partially decomposed through fermentation. 5.6.2 Pesticides These are used for mitigation, controlling or eliminating pests troublesome to crops or livestock. These include insecticides, fungicides, fumigants, herbicides, rodenticides and various other materials mostly synthetic chemical produced in concentrated form but diluted for application with various substances such as water, talc, clays, kerosene and the like. 5.7 Large Scale Farms These are farms with at least 20 hectares of cultivated land or 50 heads of cattle or 100 goats/sheep/pigs or 1,000 chicken. In addition to this, they should fulfil all of the four listed conditions.  Greater part of the produce should go to the market;  Operation of farm should be continuous;  There should be an application of machinery/implements on the farm; and  Should have at least one permanent employee. 33 5.8 Small Scale Farms Is an individual or organization that exercises management control over the agricultural operation and who takes major decisions regarding resource utilization or funding/disbursements. 5.9 Operator Is an individual or organization that exercises management control over the agricultural operation and who takes major decisions regarding resource utilization or funding/disbursements. 5.10 Masika Season Masika is a Kiswahili word referring to long rain season covering the months of March through May, of the same year. 5.11 Vuli Season Vuli is a Kiswahili word referring to short rain season covering the months of October through January of the following year. 5.12 Fish Farming Fish Farming is the rearing or harvesting of fish. It is different from fishing in that, in fish farming, the fish have to be reared and fed. Fishing traps or the natural catching occurring in rivers and the sea should not be included. 5.13 Hunting and Gathering Hunting and Gathering is the use of non-farmed resources from uncultivated land for food and/or sale (i.e. killing wild animals, collecting mushroom, berries, wild honey, or roots, etc.) 5.14 Bee Keeping Bee keeping is rearing of bees in man-made hives, normally done for harvesting of honey and other bee products. Honey gathering (wild honey) is different as no rearing activities take place. 34 CHAPTER 6 AGRICUTURAL SAMPLE CENSUS PREPARATIONS 6.1 Introduction The planning for the 2007/08 Agricultural Census started in July 2007 with the preparation of draft questionnaires which were later presented to the Stakeholders in February 2008. Work plan and budget were also prepared and presented to the Censuses and Surveys Technical Working Group for approval. Listing forms and questionnaires were prepared/finalized, design of the sampling frame, tabulation plan, pilot testing, preparation of a work plan and budget, the survey instruments, sourcing of funds, user-producer workshops were conducted and several technical committee meetings were held to assist in finalizing these activities. Information and education campaign was also launched before the data collection exercise. The census work plan was to be implemented from July 2008, but due to late disbursement of funds from MKUKUTA Basket Fund, the implementation started in September,2008 when the first disbursement was made. Additional funds started to flow in from March, 2009. The work plan had to be reviewed due to the late disbursement of funds. 6.2 Design of the Census Instruments Three types of census instruments were designed to collect the census data and information. These are: listing forms, questionnaires and instruction manuals. 6.2.1 Listing Forms The listing forms (Appendix III) were designed for the purpose of soliciting information that was able to identify the agricultural households in the selected villages. There were three types of listing forms:.  ACLF1: for listing all the names of sub-village leaders in each of the selected villages;  ACLF2: for listing all the heads of households and the total number of members in the household. The form also collected information on the number of farms, cattle by type, goats, sheep, pigs, and chicken/ducks. This was to facilitate the selection of agricultural households for interview; and. 35  ACLF3: The form was used to list the 15 selected agricultural households in each of the selected villages. The procedure of selecting the 15 households is given in Appendix 3. 6.2.2 Questionnaires The development of the census questionnaires was an important task in the data collection exercise. The census questionnaires were prepared before the enumeration to allow adequate pilot test and pre- enumeration training. Stakeholder/user group meetings were held to ensure that most of the data needs were incorporated into the questionnaires. Three types of questionnaires were used during the 2007/08 Agricultural Sample Census. These were: ACF1: Smallholder/Small Scale Farmers Questionnaire The questionnaire was administered to all selected agricultural households in the villages. ACF2: Large Scale Farmers Questionnaire The questionnaire was administered to all Large Scale Farms (commercial) in Tanzania. There was a total of 1,006 large scale farms. ACF3: Village/Community Level Questionnaire The questionnaire was used to obtain information on the villagers’ access to community resources and gate farm prices of commodities produced by the villages. 6.3.3 Instruction Manuals (Training Manual and Enumerators’ Manual) Instruction manuals are essential in securing common understanding of tasks to be performed, and providing a reference guide during enumeration, standardizing procedures to enumerators and supervisors. For the 2007/08 Agricultural Sample Census, there were two types of instruction manuals; the Enumerators’ Instruction Manual and the Training Manual. Enumerators’ Instruction Manual This manual contained detailed explanation on procedures for conducting the enumeration, interviewing techniques, guidance on how to handle major and frequently encountered problems and 36 instructions on how to fill in the questionnaires properly. The manual also contained concepts and definitions of most of the variables of which data was collected. Training Manual The training manual was used as a working document to guide the training exercise at different levels of the cascade training. The purpose of the training manual was to ensure that the training at all levels was exactly the same so as to maintain consistency. The training manual contained standard examples that were to be used by the trainers throughout the country in order to ensure uniformity. 6.4 Preparation of Tabulation Plan The census tabulation plan refers to the table list and other summary indicators that are expected to be published. Since the tabulation plan relates to the published end product, it should clearly indicate the following:  Title of each table;  Unit of measurement;  Classes adopted for characteristics studied in each table; and  Aggregate levels such as administrative units or agro-ecological regions, which involve separate tabulations. For the 2007/08 Agricultural Sample Census, the initial tabulation plan was prepared concurrently with the final stages of the questionnaire design; however, due to the large size of the dataset, the tabulation plan was continuously updated throughout the analysis stages. 6.5 User-Producer Workshop It was very important to get views and data needs from stakeholders before finalizing the questionnaires. It was necessary to work hand in hand with the respective sectors so as to achieve the objectives. 6.6 Pilot Test Pilot Test of the census instruments is one of the most important and essential preparatory activities of any census. The pilot test for the 2007/08 Agricultural Sample Census was conducted in two regions in 37 the Mainland and two regions in Zanzibar. The pilot areas were selected purposively to cover crops, livestock and coral farming areas. The main objectives of the pilot test were:  To assess the ability of the supervisors to conduct, control and monitor the census;  To assess the ability of the supervisors in administering the sampling procedures;  To test the applicability/knowledge of the Kiswahili phrases and words used in the questionnaires so as to assess the ability of the respondents in providing correct answers to the census questions;  To assess the ability of the enumerators in understanding the questionnaires and eventually in administering them to get the correct information; and  To determine the average time required for enumerating one household. 6.7 Preparation of Information, Education and Communication (IEC) Materials Information, Education and Communication are an important aspect in any census undertaking. This is due to the fact that, inadequately informed public may not be cooperative and this could jeopardize the entire census exercise. The main objective of the IEC programme for the 2007/08 Agricultural Sample Census was to sensitize and mobilize Tanzanians to support, cooperate and participate in the census exercise. The main methods used in educating the public during the census were radio announcements, leaflets, TV programmes, banners and newspaper advertisements. In addition, all the personnel who worked on the census project were given t-shirts and caps to make it easy for the respondents to identify them. In the first week of May 2009,the Minister of Agriculture, Food Security and Cooperatives launched the Census Enumeration Activity in Bunda District, Mara Region. This was one of the IEC strategies in sensitising the public to participate in the enumeration exercise. 6. 8 Census Logistics The Census required a large number of materials to be transported to the regions and districts. These included questionnaires, instructions manuals for enumerators, training manuals, listing forms, bags for enumerators and supervisors, t-shirts, caps, leaflets and stationery. All these materials had to be 38 transported directly from Dar es Salaam to all the regions and from the regions to the districts where the materials were eventually to be distributed to the enumerators. 6.8.1 Procurement of Materials and Printing of Census Instruments and IEC Materials The list and the total number of materials to be procured and documents to be printed were submitted to the Logistics Team well in advance before the enumeration. The procurement and the printing of materials followed the Public Procurement Regulations. Printing of the census instruments (manuals, questionnaires, listing forms) was done after the training of trainers so as to be able to accommodate any suggestions or opinions from the trainees. 6.8.2 Transportation and Distribution of the Census Instruments and Materials to the Regions The distribution list of the census instruments and materials was given to the Logistics Team to enable them distribute the instruments and materials adequately to the districts. The transportation of the instruments was contracted to a government agency, which transported them to all regional headquarters. From there, they were transported to the training centres using the vehicles that were assigned to each region. At the training centres, the materials and instruments were distributed to the enumerators. Materials were also transported by boat from Dar es Salaam to Zanzibar and later transported by office vehicles to the districts in Zanzibar. 6.9 Training of Field Staff Training is one of the most important aspects of undertaking a census. Training of census staff at all levels is important. The training of supervisors and enumerators is absolutely vital for the success of the census because the adequacy and intensity of the training for the staff determine the quality of the census. The objective of training the supervisors and enumerators was to impart knowledge among the field staff on the questionnaires, instructions and ideas, instil attitudes towards achieving excellence in performance and develop the skills to translate training into performance. The attainment of these objectives was in fact a test of the skills of the trainers themselves and their ability to develop training materials. 39 6.10 The National Level Training – Training of Trainers (ToT)) The Training of Trainers was held in Morogoro, whereby trainees were recruited from the National Bureau of Statistics (NBS), Ministry of Agriculture and Food Security and Cooperatives, Ministry of Water, Ministry of Livestock and Fisheries Development, Ministry of Industry and Trade, Prime Minister’s Office, Regional Administration and Local Government, Ministry of Agriculture and Natural Resources, Ministry of Livestock and Fisheries and Office of the Chief Government Statistician. The trainees at ToT were the trainers at the district level. The trainers included statisticians from the National Bureau of Statistics as well as statisticians, economists and planners from the Agricultural Sector Lead Ministries (ASLMs). 6.11 The District level Training The training was held at three convenient centres in each region and was conducted by three trainers from the national level who moved from one centre to another to ensure that standards and uniformity were maintained throughout the training exercise. During the first three days, a total of 3,192 enumerators were trained. However, this number was reduced to half (1,596 enumerators) by selecting the best enumerators who performed well in the given test. The rest of the enumerators carried out the listing exercise in their respective villages. During the enumeration exercise, enumerators who performed well carried out enumeration in two villages. About 266 district supervisors and 1,596 enumerators were therefore trained at this level. 40 CHAPTER 7 AGRICUTURAL SAMPLE CENSUS FIELD WORK 7.1 Introduction The Census field work was conducted by the Planning Group (staff from the National Bureau of Statistics of Tanzania Mainland in collaboration with the sector ministries: the Ministry of Agriculture and Food Security and Cooperatives, Ministry of Water, Ministry of Livestock and Fisheries Development, Ministry of Industry and Trade, the Prime Minister’s Office, Regional Administration and Local Government, Ministry of Agriculture and Natural Resources, Ministry of Livestock and Fisheries and the Zanzibar Office of the Chief Government Statistician). These were responsible for the overall planning of the census as well as supervision of the field work. 7.2 Field Organization To ensure a successful census, careful planning, monitoring and evaluation and supervision of the census activities including the field work is required. As such, proper field organization was very vital. Below is the field organization structure: Chart 1: Field Organization Structure Mobile Response Team Regional Supervisors/Regional Statistical Officers District Supervisors Enumerators 41 7.3 Listing Exercise The listing of households started immediately after the training of enumerators and supervisors. A total of 3,192 enumerators were involved in the listing exercise. Each enumerator was provided with the following items:  A notebook;  Two pencils;  An eraser;  A pen;  An instructions annual;  Three types of listing forms (ACLF1, ACLF2, ACLF3);  A calculator;  A plastic bag;  One t-shirt;  One cap; and  Leaflets for publicity. Each of the 1,596 best enumerators was provided with:  15 copies of the smallholder questionnaire; and  One copy of community questionnaire The listing of households started in the first week of May, 2009 in all the sampled villages and was completed by the fourth week of May, 2009. Before starting the listing exercise, the enumerators reported to the Local Authorities for self introduction and to inform the latter on the Agricultural Sample Census exercise which was to be carried out in their respective villages. The enumerators also informed the Local Authorities on the procedures to be followed during the exercise. During the listing exercise, forms ACLIF1 and ACLIF2 were used in order to get the same frame of agricultural households from which the fifteen (15) agricultural households were selected. The selection was done using random number table (Appendix 3). 42 The Regional and District supervisors were provided with motor vehicles and motorcycles respectively for the supervision work. 7.4 Enumeration Exercise 7.4.1 Smallholder enumeration Enumeration exercise for the smallholders commenced in the first week of June, 2009 in the villages and took about two weeks but in most of the villages, the enumeration could not be completed as scheduled due to a number of reasons some of which were:  Under estimation of the time required for completing the questionnaires at the planning phase of the project;  Villages being much bigger than earlier estimated, this required extensive walking from one household to the other; and.  Some enumerators lived in distant villages which required more time to travel daily to and from the sampled villages. Some district supervisors were unable to supervise all their villages due to additional work assigned by their District Office bosses. As a result, supervisors from the Head Office had to do most of the supervision work. Consistency checks were carried out both in the field and at the District Offices to ensure quality of the collected data. 7.4.2 Enumeration of Large Scale Farms Enumeration exercise of large scale farms was carried out immediately after the enumeration of small holders. District Supervisors carried out the enumeration. However, some of the large scale farms had most of their information at their respective headquarters. Questionnaires were therefore posted to their respective headquarters for completion. 7.4.3 Community Level Enumeration Community level questionnaires were completed by enumerators after enumeration of smallholder questionnaires. One questionnaire was administered to each sampled village. 43 7.5 Collection and Reception of the Filled in Census Questionnaires from the Regions The collection and reception of the filled in questionnaires from the regions took place in August, 2009. All the received questionnaires were stored at the Eastern Africa Statistical Training Centre on their arrival from the regions ready for manual editing and data entry. CHAPTER 8 CENSUS DATA PROCESSING 8.1 Data Processing After all the completed questionnaires for the 2007/08 Agricultural Sample Census were received in Dar es Salaam, the next exercise was to commence data processing. In order to successfully achieve this, the planning team had to make sure that all the necessary logistics and materials were in place. This mandatory preparation included:  The preparation of special package boxes each for storing questionnaires for one PSU or a village;  The preparation of manual editing routines;  The selection and design of a proper data capture and cleaning mechanism;  The design of a formatting application;  The setting up of an SPSS for handling the data; and  The preparation of sampling weights. 8.2 Manual Editing of the Completed Census Questionnaires The Planning team found it necessary to manually edit all the completed questionnaires. Editing in the office was not meant to alter what was collected but to properly write the data so as to be able to be scanned. It was an utmost important exercise so as to prepare the questionnaires for the scanning exercise. The main activities during this exercise were to:  Make sure that each of the filled in questionnaire has the correct identification;  Make sure that each of the filled in questionnaire has all the required number of pages;  Remove any distortions and wrinkles on the questionnaires which resulted from their mishandling during field work; and.  Remove any obstacles that could be a barrier during either scanning or extraction exercise. 44 8.3 Scanning and Data Capture The Census Data Capture exercise was performed using the OCR scanning technology. The decision to use the scanning process was made to ensure that the quality of the census data was free from key stroke errors at all stages. To implement the scanning technology, two main stages were involved:  Scanning a process of taking an electronic picture of the form (one page of a questionnaire) and stores it as a graphic image in the computer; and  Recognition of a process of capturing the data from the scanned image and storing it in an ASCII file. 8.3.1 Scanning and Questionnaire Handling The main stages in implementing the scanning exercise were: Questionnaire Filing and Storage Organization and control of questionnaires into and out of the filing room Guillotine Operation The guillotine operation cuts the bound edge of all questionnaires for a village (15 questionnaires, one batch in one operation). Scanning Operation The scanner took an electronic picture of each of the detached pages of the questionnaire for a village. A batch of 15 questionnaires took about 2 minutes to scan. As the sheets were scanned, the images were displayed in a computer. The software was programmed to detect that each village had 15 questionnaires and that each questionnaire had 19 pages. As the scanning proceeded, the counter on the screen displayed the number of pages that have been scanned in a questionnaire and the number of scanned questionnaires. The procedure made it virtually impossible to miss a page or the whole questionnaire from a batch. Binding Operation 45 Every completed batch was handled to a Binding Operator to bind all the 15 questionnaires in one book and the batch was returned to the Questionnaire Filing and Storage Section for recording and filing. Process in OCR Data Capture There were two main operational processes: Scanning and Recognition The scanning process took an electronic picture of the form and stored it as a graphic file on a computer. 8.3.2 Optical Character Recognition Scanning and Data Extraction There were two main types of automatic processes: Mark Recognition this is the recognition of shaded circles or marks (called blobs) on a form. The positioning of these blobs on the form determines the alphanumerical character it represents. Character Recognition This is the recognition of alphanumerical characters on a form. Optical Character Recognition (OCR) is the capture of machine printed text from questionnaires. 8.3.3 Lessons learned from the Scanning/Extraction Process Aspects of Questionnaire Design  Excel is an excellent application for designing questionnaires for scanning due to its tabular structure,  Ensure all answers in the questionnaire are numeric codes. Do not try to extract textual data.  Spend time emphasizing scanning hand writing. Enumerators must practice this and a test should be given to the enumerators before accepting their appointments.  Drop out and colour printing.  Registration points make sure that the questionnaire has many registration points and they should vary in size and shape.  For null response leave blank NOT zero.  Boxes in the questionnaire should be of the same size.  Consider using perforation instead of a guillotine. 46  Use one printing company and make sure that the quality is good and consistent. Faded questionnaires increase the number of dropouts.  Print all questionnaires in the same batch and on the same printer and make sure more than what is required is printed. At least an additional five percent (5%). Aspects of Extraction Template Design  Programming of validation routines for each variable is essential.  Complex validation between variables can be also be done, however this can bring the extraction process to a halt.  Thoroughly test the system with a proper pilot.  Start with manual extraction until a high level of recognition is reached before switching to automatic recognition.  Ensure supervision is of a higher level and that the verifiers have been trained well.  Take regular samples to manually check the data output file with the questionnaire.  It is the extraction process and NOT scanning where the bottlenecks occur. It is better to increase the number of extraction stations than buy additional scanners. 8.4 Design of a Data Structure Formatting Application This application was designed for the purpose of reformatting the structure of the OCR output to be compatible with the validation application which was designed in CSPro.It was very important to create the formatting program because it linked the OCR output with CSPro for validation and thereafter into SPSS software which was used for analysis and tabulation of the census data set. 8.5 Data Validation and Cleaning Exercise This is a computer aided process for detecting and correcting errors which were not noticed during the manual editing exercise. This was intensive as well as extensive exercise undertaken during the operation of the census. Because of the complexity of the data set, a two tier validation process was adopted whereby a team of three supervisors checked the work of the main validation team in order to improve the accuracy and consistency of the data. Setup proved to be efficient, time saving and contributed in ensuring that high quality data was obtained throughout the exercise. The validation program was prepared using CSPro application software. 47 8.6 Tabulation of the Census Data Analytical tables were generated using SPSS software according to a pre-defined tabulation plan which was prepared by the Census Planning Group. Prior to commencement of this activity, the cleaned data set was exported from CSPro to SPSS. A master file which contained the weight variables and all the geographical information was created. This was used to calculate Districts, Regional and National estimates. This file was therefore used to produce different appropriate tables according to the pre set tabulation plan. The process of the data processing is illustrated below. 48 Chart 3: Logical Framework of Data Processing 8.7 Application of Sampling Weights The 2007/08 Agricultural Sample Census was carried out on a sample basis, it was therefore necessary to apply the sampling factors for the purpose of having a true representation of the results at different levels of disaggregation (national, regional and district). In this respect, all the calculations for all the analytical tables were subjected to a “Sample Weight” according to a Pre defined Sample Weight Calculation Formulae; refer to Chapter 4 for further details on the sampling design. Verified Data (scanned) Manual entered data Structure Formatting Appliction (VBA) Validation Application (CSPro) File Handling Appplication (DOS) Tabulation Reports Export/Import Application (Cspro/SPSS 49 CHAPTER 9 RECOMMENDATIONS AND CONCLUSION 9.1 Recommendations  It is vital to maintain the current sequence of conducting an agricultural census after every five years as it provides time series data for planning, policy formulation, decision making and others.  The remuneration of enumerators should be according to the government circulars and reviewed whenever new government circulars are issued  Capacity building should be strengthened in the area of Statistical Packages  The government should consider full funding (if possible) of its statistical projects so as to avoid too much donor dependency which results in late project commencement and late disbursement of funds 9.2 Conclusion The 2007/08 National Sample Census of Agriculture was conducted in the sampled rural villages of Tanzania. The census was a comprehensive exercise to be carried out in the country and it is in line with the FAO 2010 World Round of Agricultural Census. It is the second time to carry out such a large sample census in Tanzania after the one that took place in 2002/03. The census has provided an opportunity to improve quality and quantity of the available data for investment in the agricultural sector due to its scope and coverage. The data are a key to agricultural development in the face of anticipated population growth and urbanization and have provided an opportunity to monitor and evaluate the performance of ASDP, MKUKUTA II AND MKUZA programmes. In addition, the census exercise has provided an avenue of building capacities of indigenous staff in data collection, processing and analysis. The census data were collected from agricultural households representing one third of the total rural villages. Urban villages were not included in the sample; the data should therefore be used with caution, as the contribution of urban agricultural production to the total national agricultural production especially in food and cash crops is minimal. However, in the urban settlement there are considerable activities especially in horticultural crop production, eggs, milk and broiler production from exotic breeds, all of which are not reflected in the data. 50 Appendix 1 AGRICULTURAL SAMPLE CENSUS 2008 LIST OF SAMPLED VILLAGES Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 1 Dodoma 1 1 Kondoa 11 Bumbuta 5 Itaswi 11011005 1 4174 1 Dodoma 1 1 Kondoa 21 Pahi 6 Kinyasi Kati 11021006 1 3652 1 Dodoma 1 1 Kondoa 31 Busi 3 Keikei 11031003 1 3454 1 Dodoma 1 1 Kondoa 51 Kalamba 2 Kalamba 11051002 1 5380 1 Dodoma 1 1 Kondoa 51 Kalamba 4 Loo 11051004 1 3034 1 Dodoma 1 1 Kondoa 81 Dalai 2 Tandala 11081002 1 4864 1 Dodoma 1 1 Kondoa 91 Jangalo 2 Itolwa - Part I 11091002 1 6372 1 Dodoma 1 1 Kondoa 101 Mrijo 2 Mrijo Chini 11101002 1 3259 1 Dodoma 1 1 Kondoa 111 Chandama 1 Mapango 11111001 1 3080 1 Dodoma 1 1 Kondoa 121 Goima 1 Goima 11121001 1 2914 1 Dodoma 1 1 Kondoa 121 Goima 5 Songolo 11121005 1 6631 1 Dodoma 1 1 Kondoa 141 Paranga 2 Kelema Balai 11141002 1 3589 1 Dodoma 1 1 Kondoa 151 Gwandi 2 Rofati 11151002 1 1476 1 Dodoma 1 1 Kondoa 181 Sanzawa 1 Sanzawa 11181001 1 4070 1 Dodoma 1 1 Kondoa 191 Kwamtoro 1 Kwamtoro 11191001 1 2083 1 Dodoma 1 1 Kondoa 211 Suruke 2 Tungufu 11211002 1 1025 1 Dodoma 1 1 Kondoa 221 Kingale 2 Tampori 11221002 1 1192 1 Dodoma 1 1 Kondoa 241 Kolo 1 Kolo 11241001 1 3383 1 Dodoma 1 1 Kondoa 261 Thawi 1 Sakami 11261001 1 4473 1 Dodoma 1 1 Kondoa 261 Thawi 3 Thawi Juu 11261003 1 1068 1 Dodoma 1 1 Kondoa 281 Soera 2 Bukulu 11281002 1 3429 1 Dodoma 1 1 Kondoa 281 Soera 3 Humai 11281003 1 1060 1 Dodoma 1 1 Kondoa 301 Kikilo 2 Ororimo 11301002 1 1941 1 Dodoma 1 1 Kondoa 311 Bereko 2 Bereko 11311002 1 4584 1 Dodoma 1 1 Kondoa 331 Kikore 1 kikore 11331001 1 2106 1 Dodoma 1 1 Kondoa 341 Makorongo 1 Maziwa 11341001 1 2627 1 Dodoma 1 1 Kondoa 351 Ovada 4 Ovada 11351004 1 2142 1 Dodoma 2 2 Mpwapwa 11 Mazae 2 Gulwe 12011002 1 3878 1 Dodoma 2 2 Mpwapwa 11 Mazae 5 Kisokwe 12011005 1 3894 1 Dodoma 2 2 Mpwapwa 23 Vin'ghawe 3 Isinghu 12023003 1 2208 1 Dodoma 2 2 Mpwapwa 31 Matomondo 3 Mlembule 12031003 1 3079 1 Dodoma 2 2 Mpwapwa 41 Kimagai 2 Bumila 12041002 1 2716 1 Dodoma 2 2 Mpwapwa 53 Kibakwe 1 Lukole 12053001 1 3562 1 Dodoma 2 2 Mpwapwa 53 Kibakwe 3 Iyenge 12053003 1 4660 1 Dodoma 2 2 Mpwapwa 61 Lumuma 1 Pwaga 12061001 1 6987 1 Dodoma 2 2 Mpwapwa 61 Lumuma 4 Lufusi 12061004 1 704 1 Dodoma 2 2 Mpwapwa 71 Luhundwa 1 Ikuyu 12071001 1 4382 51 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 1 Dodoma 2 2 Mpwapwa 71 Luhundwa 4 Lufu 12071004 1 2502 1 Dodoma 2 2 Mpwapwa 81 Massa 3 Chogola 12081003 1 3276 1 Dodoma 2 2 Mpwapwa 91 Ipera 1 Kinusa 12091001 1 4015 1 Dodoma 2 2 Mpwapwa 91 Ipera 6 Kikuyu 12091006 1 2229 1 Dodoma 2 2 Mpwapwa 101 Rudi 3 Mtamba 12101003 1 2986 1 Dodoma 2 2 Mpwapwa 101 Rudi 5 Mtera 12101005 1 3088 1 Dodoma 2 2 Mpwapwa 111 Mlunduzi 2 Chinya Nghuku 12111002 1 2485 1 Dodoma 2 2 Mpwapwa 111 Mlunduzi 5 Seluka 12111005 1 1931 1 Dodoma 2 2 Mpwapwa 121 Wotta 3 Lwihomero 12121003 1 3518 1 Dodoma 2 2 Mpwapwa 131 Mima 1 Mima 12131001 1 4101 1 Dodoma 2 2 Mpwapwa 131 Mima 4 Chamanda 12131004 1 1491 1 Dodoma 2 2 Mpwapwa 141 Berege 1 Berege 12141001 1 4524 1 Dodoma 2 2 Mpwapwa 141 Berege 4 Kibwegere 12141004 1 2603 1 Dodoma 2 2 Mpwapwa 151 Chunyu 2 Nhambi 12151002 1 5103 1 Dodoma 2 2 Mpwapwa 151 Chunyu 4 Msagali 12151004 1 6236 1 Dodoma 2 2 Mpwapwa 161 Mbuga 2 Mbuga 12161002 1 3711 1 Dodoma 2 2 Mpwapwa 171 Godegode 3 Mgoma 12171003 1 1411 1 Dodoma 3 3 Kongwa 13 Kongwa 2 Chimlata 13013002 1 652 1 Dodoma 3 3 Kongwa 21 Sejeli 2 Sejeli 13021002 1 1990 1 Dodoma 3 3 Kongwa 31 Hogoro 1 Songambele A 13031001 1 6636 1 Dodoma 3 3 Kongwa 31 Hogoro 2 Songambele B 13031002 1 5187 1 Dodoma 3 3 Kongwa 31 Hogoro 4 Chamae 13031004 1 2744 1 Dodoma 3 3 Kongwa 31 Hogoro 6 Banyibanyi 13031006 1 5574 1 Dodoma 3 3 Kongwa 41 Zoissa 3 Leganga 13041003 1 1972 1 Dodoma 3 3 Kongwa 51 Mkoka 4 Makawa 13051004 1 4929 1 Dodoma 3 3 Kongwa 51 Mkoka 6 Mkoka 13051006 1 7508 1 Dodoma 3 3 Kongwa 61 Njoge 1 Ngomai 13061001 1 6143 1 Dodoma 3 3 Kongwa 61 Njoge 3 Hemba hemba 13061003 1 2636 1 Dodoma 3 3 Kongwa 71 Mtanana 2 Ndalibo 13071002 1 4259 1 Dodoma 3 3 Kongwa 81 Pandambili 1 Pandambili 13081001 1 6445 1 Dodoma 3 3 Kongwa 81 Pandambili 3 Kiteto 13081003 1 3354 1 Dodoma 3 3 Kongwa 81 Pandambili 5 Moleti 13081005 1 5406 1 Dodoma 3 3 Kongwa 81 Pandambili 7 Vihingo 13081007 1 2607 1 Dodoma 3 3 Kongwa 91 Mlali 1 Mlali 13091001 1 7550 1 Dodoma 3 3 Kongwa 91 Mlali 3 Mlali Bondeni 13091003 1 6302 1 Dodoma 3 3 Kongwa 91 Mlali 5 Nghumbi 13091005 1 5327 1 Dodoma 3 3 Kongwa 101 Iduo 2 Chang'ombe 13101002 1 1851 1 Dodoma 3 3 Kongwa 111 Sagara 1 Sagara 13111001 1 8616 1 Dodoma 3 3 Kongwa 111 Sagara 3 Laikala 13111003 1 3713 1 Dodoma 3 3 Kongwa 123 Kibaigwa 2 Ndurugumi 13123002 1 3998 1 Dodoma 3 3 Kongwa 133 Ugogoni 2 Machenje 13133002 1 2639 52 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 1 Dodoma 3 3 Kongwa 133 Ugogoni 4 Ibwaga 13133004 1 4263 1 Dodoma 3 3 Kongwa 141 Chamkoroma 1 Chamkoroma 13141001 1 4402 1 Dodoma 3 3 Kongwa 141 Chamkoroma 4 Manghweta 13141004 1 1955 1 Dodoma 5 5 Dodoma Urb 71 Msalato 1 Msalato A 15071001 1 3476 1 Dodoma 5 5 Dodoma Urb 81 Makutopora 1 Veyula 15081001 1 6846 1 Dodoma 5 5 Dodoma Urb 81 Makutopora 3 Mchemwa 15081003 1 2013 1 Dodoma 5 5 Dodoma Urb 91 Chihanga 3 Nzasa 15091003 1 4907 1 Dodoma 5 5 Dodoma Urb 101 Hombolo 1 Hombolo Makulu - Maseya 15101001 1 4894 1 Dodoma 5 5 Dodoma Urb 101 Hombolo 2 Hombolo Bwawani - Kolimba 15101002 1 7735 1 Dodoma 5 5 Dodoma Urb 101 Hombolo 4 Zesipa 15101004 1 3616 1 Dodoma 5 5 Dodoma Urb 111 Ipala 1 Ipala 15111001 1 5159 1 Dodoma 5 5 Dodoma Urb 111 Ipala 3 Mahoma Makulu 15111003 1 1758 1 Dodoma 5 5 Dodoma Urb 121 Nzuguni 2 Kitelela 15121002 1 1749 1 Dodoma 5 5 Dodoma Urb 131 Dodoma Makulu 1 Dodoma Makulu 15131001 1 2113 1 Dodoma 5 5 Dodoma Urb 141 Mtumba 1 Mtumba 15141001 1 2745 1 Dodoma 5 5 Dodoma Urb 141 Mtumba 3 Ihumwa 15141003 1 8168 1 Dodoma 5 5 Dodoma Urb 151 Kikombo 1 Kikombo 15151001 1 4463 1 Dodoma 5 5 Dodoma Urb 151 Kikombo 2 Chololo 15151002 1 3442 1 Dodoma 5 5 Dodoma Urb 161 Ng'hong'ona 1 Ng'hong'onha 15161001 1 5344 1 Dodoma 5 5 Dodoma Urb 171 Mpunguzi 1 Mpunguzi 15171001 1 7619 1 Dodoma 5 5 Dodoma Urb 171 Mpunguzi 2 Matumbulu 15171002 1 5168 1 Dodoma 5 5 Dodoma Urb 171 Mpunguzi 3 Nkulabi 15171003 1 3357 1 Dodoma 5 5 Dodoma Urb 221 Mkonze 1 Mkonze 15221001 1 3967 1 Dodoma 5 5 Dodoma Urb 221 Mkonze 2 Michese 15221002 1 3860 1 Dodoma 5 5 Dodoma Urb 231 Mbabala 1 Mbabala A 15231001 1 7351 1 Dodoma 5 5 Dodoma Urb 241 Zuzu 1 Zuzu - Majengo,Mbuyuni 15241001 1 3645 1 Dodoma 5 5 Dodoma urb 291 Nala 1 Nala 15291001 1 4374 1 Dodoma 5 5 Dodoma urb 291 Nala 2 Chigongwe 15291002 1 5414 1 Dodoma 5 5 Dodoma Urb 301 Mbalawala 1 Mbalawala 15301001 1 3466 1 Dodoma 5 5 Dodoma Urb 301 Mbalawala 2 Lugala 15301002 1 4077 1 Dodoma 4 6 Bahi 241 Mwitikira 1 Mwitikila 16241001 0 3026 1 Dodoma 4 6 Bahi 301 Mpalanga 2 Mpalanga 16301002 0 3951 1 Dodoma 4 6 Bahi 311 Chibelela 1 Isangha 16311001 1 4299 1 Dodoma 4 6 Bahi 321 Mtitaa 1 Mtitaa 16321001 0 4333 1 Dodoma 4 6 Bahi 331 Ibugule 1 Ibugule 16331001 1 1836 1 Dodoma 4 6 Bahi 341 Nondwa 1 Nondwa 16341001 0 2446 1 Dodoma 4 6 Bahi 341 Nondwa 3 Magaga 16341003 1 1652 1 Dodoma 4 6 Bahi 351 Chali 1 Chali Igongo 16351001 0 2138 1 Dodoma 4 6 Bahi 351 Chali 4 Chikopelo 16351004 1 2928 53 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 1 Dodoma 4 6 Bahi 361 Chipanga 2 Chipanga 'B' 16361002 0 2927 1 Dodoma 4 6 Bahi 371 Chikola 1 Michendeli 16371001 1 6039 1 Dodoma 4 6 Bahi 371 Chikola 3 Chikola 16371003 1 2736 1 Dodoma 4 6 Bahi 381 Bahi 1 Uhelela 16381001 0 1358 1 Dodoma 4 6 Bahi 381 Bahi 3 Nagulo Bahi 16381003 1 4965 1 Dodoma 4 6 Bahi 391 Mpamantwa 2 Mpamatwa 16391002 0 3673 1 Dodoma 4 6 Bahi 401 Ibihwa 1 Mkola 16401001 1 3561 1 Dodoma 4 6 Bahi 411 Kigwe 1 Kingwe 16411001 1 5974 1 Dodoma 4 6 Bahi 411 Kigwe 2 Mpinga 16411002 0 4799 1 Dodoma 4 6 Bahi 421 Ilindi 1 Mindola 16421001 1 2435 1 Dodoma 4 6 Bahi 431 Makanda 2 Chode 16431002 0 1978 1 Dodoma 4 6 Bahi 441 Lamaiti 1 Bankolo 16441001 1 1940 1 Dodoma 4 6 Bahi 451 Mundemu 1 Nguji 16451001 0 1648 1 Dodoma 4 6 Bahi 451 Mundemu 2 Mundemu 16451002 1 2356 1 Dodoma 4 6 Bahi 461 Msisi 2 Mchito 16461002 0 1345 1 Dodoma 4 6 Bahi 461 Msisi 4 Tinai 16461004 1 1209 1 Dodoma 4 6 Bahi 471 Zanka 2 Mayamaya 16471002 0 3108 1 Dodoma 4 6 Bahi 481 Babayu 2 Babayu 16481002 1 3060 1 Dodoma 4 7 Chamwino 11 Haneti 4 Humekwa 17011004 0 1079 1 Dodoma 4 7 Chamwino 21 Itiso 1 Itiso 17021001 1 6995 1 Dodoma 4 7 Chamwino 31 Segala 4 Zajilwa 17031004 1 4658 1 Dodoma 4 7 Chamwino 31 Segala 5 Izava 17031005 0 4564 1 Dodoma 4 7 Chamwino 41 Dabalo 3 Chiwondo 17041003 1 2222 1 Dodoma 4 7 Chamwino 51 Membe 1 Membe 17051001 0 4375 1 Dodoma 4 7 Chamwino 61 Msanga 2 Kawawa 17061002 1 3587 1 Dodoma 4 7 Chamwino 81 Buigiri 1 Chamwino 17081001 1 6496 1 Dodoma 4 7 Chamwino 91 Majeleko 1 Majeleko 17091001 0 3479 1 Dodoma 4 7 Chamwino 101 Manchali 1 Manchali 17101001 1 4740 1 Dodoma 4 7 Chamwino 111 Ikowa 2 Makoja 17111002 0 2080 1 Dodoma 4 7 Chamwino 121 Msamalo 1 Mgunga 17121001 1 4558 1 Dodoma 4 7 Chamwino 131 Igandu 3 Chinoje 17131003 0 1445 1 Dodoma 4 7 Chamwino 141 Muungano 1 Muungano 17141001 1 4051 1 Dodoma 4 7 Chamwino 161 Handali 1 Handali 17161001 1 5674 1 Dodoma 4 7 Chamwino 173 Mvumi Mission 1 Mvumi Mission 17173001 0 1273 1 Dodoma 4 7 Chamwino 181 Makang'wa 1 Makangwa 17181001 1 5636 1 Dodoma 4 7 Chamwino 191 Idifu 2 Miganga 17191002 0 2570 1 Dodoma 4 7 Chamwino 201 Iringa Mvumi 1 Iringa Mvumi 17201001 1 6019 1 Dodoma 4 7 Chamwino 211 Manzase 2 Sasajila 17211002 1 3182 1 Dodoma 4 7 Chamwino 221 Fufu 2 Loje 17221002 0 4433 1 Dodoma 4 7 Chamwino 231 Mlowa Bwawani 2 Wiliko 17231002 1 2166 54 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 1 Dodoma 4 7 Chamwino 231 Mlowa Bwawani 4 Nkwenda/ Nyerere, Azimio 17231004 0 2154 1 Dodoma 4 7 Chamwino 251 Mpwayungu 1 Mpwayungu 17251001 1 6995 1 Dodoma 4 7 Chamwino 261 Nghambaku 2 Ndogowe 17261002 1 3094 1 Dodoma 4 7 Chamwino 281 Manda 2 Ilangali 17281002 0 3553 1 Dodoma 4 7 Chamwino 291 Huzi 1 Huzi 17291001 1 3921 2 Arusha 1 1 Monduli 23 Engutoto 2 Alarash 21023002 1 1656 2 Arusha 1 1 Monduli 31 Monduli Juu 1 Emairete 21031001 0 2299 2 Arusha 1 1 Monduli 31 Monduli Juu 3 Enguiki 21031003 1 2705 2 Arusha 1 1 Monduli 31 Monduli Juu 4 Mfereji 21031004 1 3404 2 Arusha 1 1 Monduli 41 Sepeko 1 Losimingori 21041001 0 2444 2 Arusha 1 1 Monduli 41 Sepeko 2 Repurko 21041002 1 3260 2 Arusha 1 1 Monduli 41 Sepeko 4 Lendikinya 21041004 0 2189 2 Arusha 1 1 Monduli 41 Sepeko 5 Lashaine 21041005 1 3669 2 Arusha 1 1 Monduli 41 Sepeko 6 Meserani Juu 21041006 1 3550 2 Arusha 1 1 Monduli 41 Sepeko 7 Arkatani 21041007 0 1490 2 Arusha 1 1 Monduli 51 Lolkisale 1 Meserani Chini 21051001 0 1107 2 Arusha 1 1 Monduli 51 Lolkisale 2 Naalarami 21051002 1 1450 2 Arusha 1 1 Monduli 51 Lolkisale 3 Lolkisale 21051003 1 4277 2 Arusha 1 1 Monduli 61 Moita 2 Moita-Kirolit 21061002 0 1686 2 Arusha 1 1 Monduli 61 Moita 3 Moita Bwawani 21061003 1 2020 2 Arusha 1 1 Monduli 71 Makuyuni 1 Mswakini Chini 21071001 0 1246 2 Arusha 1 1 Monduli 71 Makuyuni 2 Mswakini Juu 21071002 1 1329 2 Arusha 1 1 Monduli 71 Makuyuni 3 Naitolia 21071003 0 1303 2 Arusha 1 1 Monduli 71 Makuyuni 6 Naiti 21071006 1 1872 2 Arusha 1 1 Monduli 71 Makuyuni 7 Mbuyuni 21071007 0 2145 2 Arusha 1 1 Monduli 81 Esilalei 1 Losirwa 21081001 1 4352 2 Arusha 1 1 Monduli 81 Esilalei 3 Ortukai 21081003 0 1075 2 Arusha 1 1 Monduli 93 Mto wa Mbu 1 Migombani 21093001 1 3706 2 Arusha 1 1 Monduli 101 Selela 1 Selela 21101001 1 3705 2 Arusha 1 1 Monduli 101 Selela 2 Mbaashi 21101002 0 1397 2 Arusha 1 1 Monduli 111 Engaruka 1 Engaruka Juu 21111001 1 3873 2 Arusha 1 1 Monduli 111 Engaruka 2 Engaruka Chini 21111002 0 3378 2 Arusha 3 3 Arusha 91 Terrat 1 Nadosoito 23091001 1 3340 2 Arusha 3 3 Arusha 91 Terrat 2 Mkonoo 23091002 1 4704 2 Arusha 3 3 Arusha 103 Sokon I 1 Sokon I-Lolovono 23103001 1 1899 2 Arusha 3 3 Arusha 103 Sokon I 2 Sinon 23103002 1 1224 2 Arusha 4 4 Karatu 13 Karatu 2 Gyekrum-Lambo 24013002 1 4564 2 Arusha 4 4 Karatu 21 Endamarariek 1 Bassodowish 24021001 1 4550 2 Arusha 4 4 Karatu 21 Endamarariek 3 Getamock 24021003 1 4611 2 Arusha 4 4 Karatu 21 Endamarariek 4 Endamarariek 24021004 1 5594 55 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 2 Arusha 4 4 Karatu 31 Buger 1 Endanyawe 24031001 1 2749 2 Arusha 4 4 Karatu 41 Endabash 1 Qaru 24041001 1 6048 2 Arusha 4 4 Karatu 41 Endabash 2 Endabash 24041002 1 5015 2 Arusha 4 4 Karatu 51 Kansay 2 Kansay - Kansay 24051002 1 2683 2 Arusha 4 4 Karatu 51 Kansay 4 Ngaibara 24051004 1 2028 2 Arusha 4 4 Karatu 61 Baray 2 Mbuga Nyekundu 24061002 1 3306 2 Arusha 4 4 Karatu 61 Baray 4 Dumbechand 24061004 1 3063 2 Arusha 4 4 Karatu 71 Mang'ola 1 Endamaghay 24071001 1 2352 2 Arusha 4 4 Karatu 71 Mang'ola 2 Mang'ola Barazani 24071002 1 8086 2 Arusha 4 4 Karatu 71 Mang'ola 4 Malekchand 24071004 1 3005 2 Arusha 4 4 Karatu 81 Daa 2 Endashangwet 24081002 1 2190 2 Arusha 4 4 Karatu 81 Daa 4 Makhoromba 24081004 1 1208 2 Arusha 4 4 Karatu 91 Oldeani 1 Oldeani 24091001 1 5627 2 Arusha 4 4 Karatu 101 Qurus 1 Gongali 24101001 1 4640 2 Arusha 4 4 Karatu 101 Qurus 3 Bashay 24101003 1 7126 2 Arusha 4 4 Karatu 111 Ganako 1 Ayalabe 24111001 1 5403 2 Arusha 4 4 Karatu 111 Ganako 2 Tloma 24111002 1 4515 2 Arusha 4 4 Karatu 121 Rhotia 1 Kilimamoja 24121001 1 1945 2 Arusha 4 4 Karatu 121 Rhotia 3 Rhotia Kainam 24121003 1 2966 2 Arusha 4 4 Karatu 121 Rhotia 4 Rhotia Kati 24121004 1 5518 2 Arusha 4 4 Karatu 131 Mbulumbulu 1 Lositete 24131001 1 2233 2 Arusha 4 4 Karatu 131 Mbulumbulu 3 Slahhamo 24131003 1 5633 2 Arusha 4 4 Karatu 131 Mbulumbulu 5 Kambi ya Simba 24131005 1 4941 2 Arusha 5 5 Ngorongoro 13 Orgosorok 1 Engusero Sambu 25013001 1 5310 2 Arusha 5 5 Ngorongoro 13 Orgosorok 2 Olorien Magaiduru 25013002 1 4108 2 Arusha 5 5 Ngorongoro 21 Digodigo 2 Kisangiro 25021002 1 1664 2 Arusha 5 5 Ngorongoro 21 Digodigo 3 Digodigo 25021003 1 2397 2 Arusha 5 5 Ngorongoro 21 Digodigo 4 Yasimdito 25021004 1 1417 2 Arusha 5 5 Ngorongoro 21 Digodigo 7 Mugholo 25021007 1 1462 2 Arusha 5 5 Ngorongoro 31 Oldonyo - Sambu 1 Oldonyo Sambu 25031001 1 3256 2 Arusha 5 5 Ngorongoro 41 Pinyinyi 2 Engaresero 25041002 1 2831 2 Arusha 5 5 Ngorongoro 61 Malambo 1 Malambo 25061001 1 1351 2 Arusha 5 5 Ngorongoro 71 Nayobi 1 Nayobi 25071001 1 6429 2 Arusha 5 5 Ngorongoro 71 Nayobi 2 Nayobi Part II 25071002 1 452 2 Arusha 5 5 Ngorongoro 71 Nayobi 3 Kapenjiro 25071003 1 4130 2 Arusha 5 5 Ngorongoro 81 Nainokanoka 1 Alailelai 25081001 1 3948 2 Arusha 5 5 Ngorongoro 81 Nainokanoka 2 Bulati 25081002 1 3492 2 Arusha 5 5 Ngorongoro 81 Nainokanoka 3 Irkeepus 25081003 1 3044 2 Arusha 5 5 Ngorongoro 91 Olbalbal 1 Meshili 25091001 1 3903 2 Arusha 5 5 Ngorongoro 91 Olbalbal 2 Ngoile 25091002 1 3104 2 Arusha 5 5 Ngorongoro 103 Ngorongoro 1 Misigiyo 25103001 1 4067 56 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 2 Arusha 5 5 Ngorongoro 103 Ngorongoro 2 Oloirobi 25103002 1 2594 2 Arusha 5 5 Ngorongoro 111 Enduleni 1 Enduleni 25111001 1 6658 2 Arusha 5 5 Ngorongoro 111 Enduleni 3 Olpiro 25111003 1 1088 2 Arusha 5 5 Ngorongoro 121 Kakesio 1 Osinoni 25121001 1 2295 2 Arusha 5 5 Ngorongoro 131 Arash 1 Losoito - Maaloni 25131001 1 2656 2 Arusha 5 5 Ngorongoro 131 Arash 2 Arash 25131002 1 3630 2 Arusha 5 5 Ngorongoro 141 Soit Sambu 1 Ololosokwan 25141001 1 3222 2 Arusha 5 5 Ngorongoro 141 Soit Sambu 2 soitsambu 25141002 1 5486 2 Arusha 5 5 Ngorongoro 141 Soit Sambu 3 Soitsambu II 25141003 1 1673 2 Arusha 1 6 Longido 121 Kitumbeine 1 Orjuloongishu 26121001 1 3625 2 Arusha 1 6 Longido 121 Kitumbeine 2 Kiserian 26121002 0 1788 2 Arusha 1 6 Longido 121 Kitumbeine 3 Noondoto 26121003 1 1539 2 Arusha 1 6 Longido 121 Kitumbeine 4 Olchoroonyokie 26121004 0 1402 2 Arusha 1 6 Longido 121 Kitumbeine 6 Ilorienito 26121006 1 1867 2 Arusha 1 6 Longido 121 Kitumbeine 7 losirwa 26121007 0 1246 2 Arusha 1 6 Longido 131 Gelai Meirugoi 2 Meirugoi 26131002 1 4131 2 Arusha 1 6 Longido 141 Gelai Lumbwa 1 Alaililai 26141001 1 2327 2 Arusha 1 6 Longido 141 Gelai Lumbwa 2 Gelai Lumbwa 26141002 0 1892 2 Arusha 1 6 Longido 151 Engarenaibor 1 Sinonik 26151001 0 1916 2 Arusha 1 6 Longido 151 Engarenaibor 2 Ngoswaki 26151002 0 2072 2 Arusha 1 6 Longido 151 Engarenaibor 3 Mairowa 26151003 1 2833 2 Arusha 1 6 Longido 151 Engarenaibor 4 Mundarara 26151004 0 2486 2 Arusha 1 6 Longido 161 Matale 1 Matale' B' 26161001 1 993 2 Arusha 1 6 Longido 161 Matale 2 Matale' A ' 26161002 0 2421 2 Arusha 1 6 Longido 173 Namanga 1 Eworendeke 26173001 1 2745 2 Arusha 1 6 Longido 173 Namanga 2 Kimokuwa 26173002 0 1651 2 Arusha 1 6 Longido 183 Longido 1 Longido 26183001 1 4705 2 Arusha 1 6 Longido 183 Longido 2 Engikaret 26183002 0 2004 2 Arusha 1 6 Longido 191 Tingatinga 1 Sinya 26191001 1 2298 2 Arusha 1 6 Longido 191 Tingatinga 2 Tingatinga 26191002 0 990 2 Arusha 1 6 Longido 191 Tingatinga 3 Ngereyani 26191003 0 1459 2 Arusha 1 6 Longido 201 Ol -molog 1 Elerai 26201001 1 1299 2 Arusha 1 6 Longido 201 Ol -molog 3 Lerangwa 26201003 0 2148 2 Arusha 1 6 Longido 201 Ol -molog 4 Kitendeni 26201004 0 804 2 Arusha 1 6 Longido 201 Ol -molog 5 Irkaswa 26201005 1 2976 2 Arusha 1 6 Longido 201 Ol -molog 6 Kamwanga 26201006 0 3122 2 Arusha 2 7 Arusha R 11 Oldonyosambu 1 Losinoni - Patrumani 27011001 1 5290 2 Arusha 2 7 Arusha R 11 Oldonyosambu 5 Oldonyosambu 27011005 0 1751 2 Arusha 2 7 Arusha R 201 Moshono 1 Olkereiyan 27201001 0 3006 2 Arusha 2 7 Arusha R 211 Mlangarini 1 Kiseriani 27211001 1 4513 2 Arusha 2 7 Arusha R 211 Mlangarini 3 Manyire 27211003 0 3060 57 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 2 Arusha 2 7 Arusha R 221 Nduruma 1 Mzimuni 27221001 1 1666 2 Arusha 2 7 Arusha R 231 Oljoro 1 Mbuyuni 27231001 0 2278 2 Arusha 2 7 Arusha R 231 Oljoro 2 Oljoro 27231002 1 1507 2 Arusha 2 7 Arusha R 243 Murieti 3 Laroi 27243003 0 3390 2 Arusha 2 7 Arusha R 253 Mateves 1 Ngurbob 27253001 1 1788 2 Arusha 2 7 Arusha R 261 Kisongo 2 Engorola 27261002 0 2389 2 Arusha 2 7 Arusha R 273 Kiranyi 1 Saitabau 27273001 1 2618 2 Arusha 2 7 Arusha R 273 Kiranyi 4 Olorien 27273004 0 1872 2 Arusha 2 7 Arusha R 283 Kimnyaki 1 Olmotonyi - Maina 27283001 1 2745 2 Arusha 2 7 Arusha R 283 Kimnyaki 5 Ngaramtoni 27283005 1 2669 2 Arusha 2 7 Arusha R 293 Moivo 1 Oltulelei 27293001 0 2510 2 Arusha 2 7 Arusha R 301 Oltroto 4 Ilkirevi 27301004 1 4497 2 Arusha 2 7 Arusha R 313 Sokoni II 1 Ngiresi 27313001 0 3182 2 Arusha 2 7 Arusha R 313 Sokoni II 4 Sekei 27313004 1 2880 2 Arusha 2 7 Arusha R 321 Oltrumet 2 Ilkiushin 27321002 0 2409 2 Arusha 2 7 Arusha R 331 Musa 2 Likamba 27331002 1 3672 2 Arusha 2 7 Arusha R 341 Mwandeti 1 Engalaoni 27341001 0 3898 2 Arusha 2 7 Arusha R 341 Mwandeti 3 Losikito 27341003 1 4282 2 Arusha 2 7 Arusha R 351 Olkokola 2 Ilkurot 27351002 0 3257 2 Arusha 2 7 Arusha R 351 Olkokola 3 Olkokola 27351003 1 6464 2 Arusha 2 7 Arusha R 361 Ilkiding'a 3 Shiboro 27361003 0 2296 2 Arusha 2 7 Arusha R 371 Bangata 2 Midawe 27371002 0 1614 2 Arusha 2 8 Meru 21 Ngarenanyuki 2 Olkung'wado 28021002 1 6622 2 Arusha 2 8 Meru 21 Ngarenanyuki 3 Kisimiri chini 28021003 0 2191 2 Arusha 2 8 Meru 31 Leguruki 1 Miririny 28031001 1 2303 2 Arusha 2 8 Meru 31 Leguruki 3 Maruango 28031003 0 3287 2 Arusha 2 8 Meru 31 Leguruki 5 Nkoasenga 28031005 1 3147 2 Arusha 2 8 Meru 43 King'ori 3 Engejososia 28043003 0 2637 2 Arusha 2 8 Meru 43 King'ori 4 Kolila 28043004 1 2387 2 Arusha 2 8 Meru 53 Kikatiti 2 Kikatiti 28053002 1 4567 2 Arusha 2 8 Meru 61 Maroroni 1 Kwa Ugoro 28061001 0 4335 2 Arusha 2 8 Meru 61 Maroroni 2 Maroroni 28061002 1 3908 2 Arusha 2 8 Meru 73 Makiba 3 Patanumbe 28073003 1 3114 2 Arusha 2 8 Meru 83 Mbuguni 1 Mikungani 28083001 0 3653 2 Arusha 2 8 Meru 83 Mbuguni 4 Msitu wa Mbogo 28083004 1 1102 2 Arusha 2 8 Meru 101 Kikwe 1 Nambala 28101001 0 2286 2 Arusha 2 8 Meru 113 Maji ya chai 1 Ngurdoto 28113001 1 6559 2 Arusha 2 8 Meru 113 Maji ya chai 4 Imbaseni 28113004 0 2235 2 Arusha 2 8 Meru 123 USA river 2 Manyata 28123002 1 949 2 Arusha 2 8 Meru 133 Nkoaranga 3 Nashupu 28133003 0 3166 2 Arusha 2 8 Meru 133 Nkoaranga 5 Ngyani 28133005 1 2225 58 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 2 Arusha 2 8 Meru 141 Songoro 3 Urisho 28141003 0 2462 2 Arusha 2 8 Meru 153 Poli 1 Poli 28153001 1 1946 2 Arusha 2 8 Meru 153 Poli 2 Ndatu 28153002 0 2305 2 Arusha 2 8 Meru 161 Singisi 2 Singisi 28161002 1 5713 2 Arusha 2 8 Meru 173 Akheri 2 Patandi 28173002 0 2551 2 Arusha 2 8 Meru 181 Nkoarisambu 3 Kimundo 28181003 1 2384 2 Arusha 2 8 Meru 191 Nkanrua 1 Loita Nkomaala 28191001 0 2070 2 Arusha 2 8 Meru 191 Nkanrua 4 Amboreni/Moivaro 28191004 1 6265 3 Kilimanjaro 1 1 Rombo 11 Mamsera 3 Mamsera Chini 31011003 1 4978 3 Kilimanjaro 1 1 Rombo 23 Mahida/Holili 2 Nduduni 31023002 1 3720 3 Kilimanjaro 1 1 Rombo 31 Mengwe Manda 1 Mengwe Juu 31031001 1 2484 3 Kilimanjaro 1 1 Rombo 31 Mengwe Manda 4 Ngareni 31031004 1 1577 3 Kilimanjaro 1 1 Rombo 41 Keni/Mengeni 1 Mengeni (Kitasha) 31041001 1 3872 3 Kilimanjaro 1 1 Rombo 51 Keni/Aleni 1 Machame/Aleni 31051001 1 3370 3 Kilimanjaro 1 1 Rombo 61 Shimbi 1 Shimbi Kati 31061001 1 5249 3 Kilimanjaro 1 1 Rombo 61 Shimbi 3 Shimbimasho 31061003 1 2624 3 Kilimanjaro 1 1 Rombo 71 Makiidi 2 Makiidi 31071002 1 4604 3 Kilimanjaro 1 1 Rombo 83 Kelamfua/Mokala 2 Mokala 31083002 1 3339 3 Kilimanjaro 1 1 Rombo 91 Ushiri/Ikuini 2 Ushiri 31091002 1 4234 3 Kilimanjaro 1 1 Rombo 101 Mrao Keryo 1 Mrao 31101001 1 2793 3 Kilimanjaro 1 1 Rombo 111 Kirwa/Keni 1 Keni 31111001 1 2717 3 Kilimanjaro 1 1 Rombo 121 Katangara/Mrere 1 Mrere 31121001 1 5888 3 Kilimanjaro 1 1 Rombo 121 Katangara/Mrere 2 Karangara 31121002 1 5273 3 Kilimanjaro 1 1 Rombo 131 Kisale Masangara 3 Msaranga 31131003 1 3307 3 Kilimanjaro 1 1 Rombo 141 Olele 2 Kitowo 31141002 1 3855 3 Kilimanjaro 1 1 Rombo 141 Olele 4 Kiooti 31141004 1 3174 3 Kilimanjaro 1 1 Rombo 151 Kirongo/Samanga 3 Kirongo Chini Part I 31151003 1 6148 3 Kilimanjaro 1 1 Rombo 161 Kitirima/Kingachi 1 Kirongo Chini Part II 31161001 1 2802 3 Kilimanjaro 1 1 Rombo 161 Kitirima/Kingachi 4 Kwalakamu 31161004 1 4679 3 Kilimanjaro 1 1 Rombo 171 Ubetu kahe 1 Kingachi 31171001 1 4464 3 Kilimanjaro 1 1 Rombo 171 Ubetu kahe 3 Kahe - Part 1 31171003 1 863 3 Kilimanjaro 1 1 Rombo 181 Nanjala Reha 1 Ubetu - part 2 31181001 1 4134 3 Kilimanjaro 1 1 Rombo 181 Nanjala Reha 2 Msangai 31181002 1 6401 3 Kilimanjaro 1 1 Rombo 181 Nanjala Reha 4 Kibaoni 31181004 1 5788 3 Kilimanjaro 1 1 Rombo 193 Tarakea Motamburu 1 Nayeme 31193001 1 6614 3 Kilimanjaro 2 2 Mwanga 13 Kileo 1 Kileo 32013001 1 2987 3 Kilimanjaro 2 2 Mwanga 13 Kileo 2 Kituri - Kilimani, Proper & Mi 32013002 1 2313 3 Kilimanjaro 2 2 Mwanga 13 Kileo 3 Kivulini 32013003 1 1598 3 Kilimanjaro 2 2 Mwanga 31 Msangeni 2 Mamba - Kati & Kikweni 32031002 1 1033 3 Kilimanjaro 2 2 Mwanga 31 Msangeni 4 Simbomu 32031004 1 1676 59 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 3 Kilimanjaro 2 2 Mwanga 41 Kifula 2 Masumbeni 32041002 1 2986 3 Kilimanjaro 2 2 Mwanga 41 Kifula 3 Raa - Nganyinyi 32041003 1 2031 3 Kilimanjaro 2 2 Mwanga 51 Kighare 1 Kighare 32051001 1 1321 3 Kilimanjaro 2 2 Mwanga 51 Kighare 3 Ndanda 32051003 1 1691 3 Kilimanjaro 2 2 Mwanga 61 Kirongwe 2 Lomwe 32061002 1 1209 3 Kilimanjaro 2 2 Mwanga 61 Kirongwe 4 Mrore 32061004 1 1426 3 Kilimanjaro 2 2 Mwanga 73 Kwakoa 2 Kigonogoni 32073002 1 2155 3 Kilimanjaro 2 2 Mwanga 73 Kwakoa 3 Ngulu - Mkongea 32073003 1 1743 3 Kilimanjaro 2 2 Mwanga 83 Lembeni 2 Kisangara - Kualutu/Marungaya 32083002 1 3347 3 Kilimanjaro 2 2 Mwanga 83 Lembeni 3 Mgagao - Mjini 32083003 1 2347 3 Kilimanjaro 2 2 Mwanga 83 Lembeni 6 Kiverenge - Mbuyuni & Tambarar 32083006 1 1386 3 Kilimanjaro 2 2 Mwanga 91 Jipe 3 Kivisini 32091003 1 741 3 Kilimanjaro 2 2 Mwanga 101 Mwaniko 2 Vuchama - Mrereni, Vongo & Uba 32101002 1 2799 3 Kilimanjaro 2 2 Mwanga 101 Mwaniko 3 Mangio 32101003 1 1533 3 Kilimanjaro 2 2 Mwanga 111 Chomvu 1 Chomvu 32111001 1 1612 3 Kilimanjaro 2 2 Mwanga 111 Chomvu 3 Kimbale 32111003 1 1410 3 Kilimanjaro 2 2 Mwanga 111 Chomvu 4 Mshewa - Ngeja Kati & Mchali J 32111004 1 2098 3 Kilimanjaro 2 2 Mwanga 121 Ngujini 3 Songoa 32121003 1 619 3 Kilimanjaro 2 2 Mwanga 141 Kilomeni 1 Kilomeni 32141001 1 2452 3 Kilimanjaro 2 2 Mwanga 151 Shighatini 1 Shighatini 32151001 1 1975 3 Kilimanjaro 2 2 Mwanga 151 Shighatini 3 Vuchamandambwe - Vanja 32151003 1 1351 3 Kilimanjaro 2 2 Mwanga 151 Shighatini 5 Mkuu 32151005 1 636 3 Kilimanjaro 3 3 Same 21 Ruvu 1 Ruvu Mferejini I 33021001 1 3210 3 Kilimanjaro 3 3 Same 21 Ruvu 3 Ruvu Mferejini II 33021003 1 2107 3 Kilimanjaro 3 3 Same 31 Njoro 3 Kizungo 33031003 1 2941 3 Kilimanjaro 3 3 Same 51 Msindo 1 Duma 33051001 1 1553 3 Kilimanjaro 3 3 Same 51 Msindo 3 Mbakweni 33051003 1 2070 3 Kilimanjaro 3 3 Same 61 Mshewa 2 Marindi 33061002 1 2005 3 Kilimanjaro 3 3 Same 71 Mhezi 1 Mtunguja 33071001 1 2574 3 Kilimanjaro 3 3 Same 83 Mwembe 1 Chajo 33083001 1 2243 3 Kilimanjaro 3 3 Same 91 Vudee 1 Vudee 33091001 1 1743 3 Kilimanjaro 3 3 Same 91 Vudee 4 Ndolwa 33091004 1 1176 3 Kilimanjaro 3 3 Same 101 Vuje 3 Vuje 33101003 1 3472 3 Kilimanjaro 3 3 Same 111 Bombo 2 Mjema 33111002 1 1354 3 Kilimanjaro 3 3 Same 121 Mtii 3 Mafiringo 33121003 1 1371 3 Kilimanjaro 3 3 Same 121 Mtii 6 Vumba 33121006 1 1339 60 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 3 Kilimanjaro 3 3 Same 133 Maore 1 Mheza 33133001 1 3337 3 Kilimanjaro 3 3 Same 143 Ndungu 1 Msufini 33143001 1 1770 3 Kilimanjaro 3 3 Same 161 Bendera 1 Mgandu 33161001 1 2081 3 Kilimanjaro 3 3 Same 171 Myamba 1 Myamba 33171001 1 2764 3 Kilimanjaro 3 3 Same 171 Myamba 5 Mang'a - Kiranga 33171005 1 1943 3 Kilimanjaro 3 3 Same 181 Mpinji 3 Sambweni 33181003 1 1977 3 Kilimanjaro 3 3 Same 191 Bwambo 2 Mweteni 33191002 1 3110 3 Kilimanjaro 3 3 Same 201 Vunta 1 Vunta 33201001 1 2173 3 Kilimanjaro 3 3 Same 201 Vunta 4 Kidunda 33201004 1 607 3 Kilimanjaro 3 3 Same 211 Chome 3 Gwang'a 33211003 1 1810 3 Kilimanjaro 3 3 Same 221 Suji 3 Gonjanza 33221003 1 2595 3 Kilimanjaro 3 3 Same 243 Hedaru 1 Gavao 33243001 1 1755 3 Kilimanjaro 3 3 Same 251 Kirangare 1 Kirangare 33251001 1 1511 3 Kilimanjaro 4 4 Moshi 'R' 11 Mwika Kusini 3 Kondeni 34011003 1 2977 3 Kilimanjaro 4 4 Moshi 'R' 21 Mwika kaskazini 3 Mrimbo(Uuwo) 34021003 1 4445 3 Kilimanjaro 4 4 Moshi 'R' 31 Mamba kaskazini 1 Mboni 34031001 1 2470 3 Kilimanjaro 4 4 Moshi 'R' 51 Marangu Mashariki 1 Sembeti 34051001 1 2038 3 Kilimanjaro 4 4 Moshi 'R' 51 Marangu Mashariki 5 Lyasongoro 34051005 1 3944 3 Kilimanjaro 4 4 Moshi 'R' 61 Marangu Magharibi 4 Komela 34061004 1 1733 3 Kilimanjaro 4 4 Moshi 'R' 73 Makuyuni 1 Lotima 34073001 1 2022 3 Kilimanjaro 4 4 Moshi 'R' 91 Kilema Kusini 3 Kilema Pofo 34091003 1 3936 3 Kilimanjaro 4 4 Moshi 'R' 101 Kirua Vunjo Mashariki 1 Mero 34101001 1 2546 3 Kilimanjaro 4 4 Moshi 'R' 111 Kirua Vunjo Magharibi 7 Iwa 34111007 1 2524 3 Kilimanjaro 4 4 Moshi 'R' 121 Kahe 3 Kisangesangeni 34121003 1 2196 3 Kilimanjaro 4 4 Moshi 'R' 141 Old Moshi East 3 Kikarara 34141003 1 2033 3 Kilimanjaro 4 4 Moshi 'R' 151 Old moshi West 3 Mandaka Mnono 34151003 1 2009 3 Kilimanjaro 4 4 Moshi 'R' 171 Uru Mashariki 2 Mnini 34171002 1 2334 3 Kilimanjaro 4 4 Moshi 'R' 181 Uru Shimbwe 1 Shimbwe Juu 34181001 1 2568 3 Kilimanjaro 4 4 Moshi 'R' 191 Uru South Mawela 6 Longuo 34191006 1 2346 3 Kilimanjaro 4 4 Moshi 'R' 201 Uru Kaskazini 4 Njari 34201004 1 3125 3 Kilimanjaro 4 4 Moshi 'R' 221 Arusha Chini 1 Kiyungi 34221001 1 859 3 Kilimanjaro 4 4 Moshi 'R' 221 Arusha Chini 6 Langasani 34221006 1 2400 3 Kilimanjaro 4 4 Moshi 'R' 241 Kibosho Kati 1 Maua 34241001 1 2212 3 Kilimanjaro 4 4 Moshi 'R' 251 Kibosho Magharibi 1 Manushi Ndoo 34251001 1 3330 3 Kilimanjaro 4 4 Moshi 'R' 251 Kibosho Magharibi 3 Kombo 34251003 1 1542 3 Kilimanjaro 4 4 Moshi 'R' 261 Kindi 2 Kindi kati 1 34261002 1 7026 3 Kilimanjaro 4 4 Moshi 'R' 271 Kirua Vunjo Kusini 1 Yamu Makaa 34271001 1 3648 3 Kilimanjaro 4 4 Moshi 'R' 281 Kirima 2 Kirima Kati 34281002 1 4148 3 Kilimanjaro 4 4 Moshi 'R' 291 Okaoni Kibosho 5 Mkomilo 34291005 1 1415 61 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 3 Kilimanjaro 4 4 Moshi 'R' 311 Kilema Kati 3 Mkyashi 34311003 1 1731 3 Kilimanjaro 5 5 Hai 11 Machame Mashariki 1 Kilanya 35011001 1 1985 3 Kilimanjaro 5 5 Hai 11 Machame Mashariki 2 Lyamungo Sinde 35011002 0 2372 3 Kilimanjaro 5 5 Hai 11 Machame Mashariki 5 Tela 35011005 1 2991 3 Kilimanjaro 5 5 Hai 11 Machame Mashariki 6 Usari / Isareni 35011006 0 2447 3 Kilimanjaro 5 5 Hai 11 Machame Mashariki 7 Urori 35011007 1 3071 3 Kilimanjaro 5 5 Hai 21 Machame Kusini 2 Longoi - Nguzo Moja 35021002 1 3228 3 Kilimanjaro 5 5 Hai 21 Machame Kusini 3 Shiri - Njoro 35021003 1 1939 3 Kilimanjaro 5 5 Hai 21 Machame Kusini 4 Shiri - Mgungani 35021004 0 4548 3 Kilimanjaro 5 5 Hai 21 Machame Kusini 7 Mijongweni Chini 35021007 1 3485 3 Kilimanjaro 5 5 Hai 31 Machame Kaskazini 1 Foo - Nkwashua 35031001 0 4907 3 Kilimanjaro 5 5 Hai 31 Machame Kaskazini 2 Wari - Rengua 35031002 1 4351 3 Kilimanjaro 5 5 Hai 31 Machame Kaskazini 4 Nshara 35031004 1 5810 3 Kilimanjaro 5 5 Hai 41 Machame Magharibi 2 Kyeeri - Sinde 35041002 1 2759 3 Kilimanjaro 5 5 Hai 51 Machame Uroki 2 Mamba 35051002 1 2124 3 Kilimanjaro 5 5 Hai 51 Machame Uroki 3 Uswaa 35051003 0 3556 3 Kilimanjaro 5 5 Hai 61 Masama Mashariki 1 Roo 35061001 1 5496 3 Kilimanjaro 5 5 Hai 61 Masama Mashariki 2 Mudio 35061002 1 6050 3 Kilimanjaro 5 5 Hai 61 Masama Mashariki 6 Sawe - Ituuni 35061006 0 2628 3 Kilimanjaro 5 5 Hai 71 Masama Magharibi 1 Lukani 35071001 1 1342 3 Kilimanjaro 5 5 Hai 71 Masama Magharibi 4 Kyuu - Loriko 35071004 0 2178 3 Kilimanjaro 5 5 Hai 71 Masama Magharibi 5 Nkwansira 35071005 1 2432 3 Kilimanjaro 5 5 Hai 71 Masama Magharibi 9 Mbosho 35071009 1 1966 3 Kilimanjaro 5 5 Hai 83 Masama Kusini 1 Mungushi 35083001 0 2316 3 Kilimanjaro 5 5 Hai 83 Masama Kusini 2 Kware 35083002 1 3181 3 Kilimanjaro 5 5 Hai 123 Masama Rundugai 3 Kawaya 35123003 0 3110 3 Kilimanjaro 5 5 Hai 123 Masama Rundugai 4 Mkalama 35123004 1 2629 3 Kilimanjaro 5 5 Hai 123 Masama Rundugai 8 Sanya Station 35123008 1 2430 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 1 Kashashi 37091001 0 2660 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 2 Kyengia 37091002 0 1646 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 3 Mae 37091003 1 2240 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 4 Wanri 37091004 0 2926 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 5 Lawate 37091005 0 2725 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 6 Manio 37091006 0 1438 3 Kilimanjaro 5 7 Siha 91 Siha Mashariki 7 Kishisha 37091007 1 1283 3 Kilimanjaro 5 7 Siha 103 Siha Kati 2 Donyomuruak 37103002 0 2712 3 Kilimanjaro 5 7 Siha 103 Siha Kati 4 Olkolili 37103004 1 3795 3 Kilimanjaro 5 7 Siha 103 Siha Kati 6 Makiwaru 37103006 0 2533 3 Kilimanjaro 5 7 Siha 103 Siha Kati 8 Ngaritati 37103008 0 2202 3 Kilimanjaro 5 7 Siha 103 Siha Kati 9 Naibilie 37103009 0 4215 3 Kilimanjaro 5 7 Siha 103 Siha Kati 10 Karansi 37103010 0 3804 62 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 3 Kilimanjaro 5 7 Siha 103 Siha Kati 12 Lekurumuni 37103012 0 1305 3 Kilimanjaro 5 7 Siha 103 Siha Kati 13 Magadini 37103013 1 2396 3 Kilimanjaro 5 7 Siha 113 Siha Magharibi 3 Ngarenairobi 37113003 0 7065 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 1 Koboko 37141001 0 4516 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 2 Mowo 37141002 1 1819 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 3 Ngarony 37141003 0 1060 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 4 Mese 37141004 0 508 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 5 Samaki Maini 37141005 0 1084 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 6 Nsherehehe 37141006 0 837 3 Kilimanjaro 5 7 Siha 141 Siha Kaskazini 7 Nrao Kisangara 37141007 0 1888 4 Tanga 1 1 Lushoto 13 Lushoto 2 Kwembago 41013002 1 2168 4 Tanga 1 1 Lushoto 31 Kwai 1 Kwemakame 41031001 1 3927 4 Tanga 1 1 Lushoto 41 Ubiri 1 Kwemashai 41041001 1 3357 4 Tanga 1 1 Lushoto 61 Vuga 1 Vuga Bagai 41061001 1 1465 4 Tanga 1 1 Lushoto 61 Vuga 6 Kiluwai 41061006 1 1699 4 Tanga 1 1 Lushoto 71 Mponde 2 Kwemhafa 41071002 1 2478 4 Tanga 1 1 Lushoto 101 Tamota 3 Msamaka 41101003 1 1478 4 Tanga 1 1 Lushoto 101 Tamota 9 Kwemakonko 41101009 1 1026 4 Tanga 1 1 Lushoto 121 Funta 2 Funta 41121002 1 2509 4 Tanga 1 1 Lushoto 131 Mayo 2 Kwabosa 41131002 1 2169 4 Tanga 1 1 Lushoto 151 Milingano 2 Bumba 41151002 1 2904 4 Tanga 1 1 Lushoto 161 Mgwashi 3 Nkongoi 41161003 1 2149 4 Tanga 1 1 Lushoto 171 Mtae 3 Panga 41171003 1 1928 4 Tanga 1 1 Lushoto 181 Sunga 1 Mambo 41181001 1 5484 4 Tanga 1 1 Lushoto 191 Rangwi 1 Goka 41191001 1 2007 4 Tanga 1 1 Lushoto 191 Rangwi 5 Nkelei 41191005 1 2278 4 Tanga 1 1 Lushoto 211 Lunguza 3 Tewe 41211003 1 1482 4 Tanga 1 1 Lushoto 221 Mbaramo 4 Nkombo 41221004 1 1591 4 Tanga 1 1 Lushoto 251 Mwangoi 1 Dule 41251001 1 3038 4 Tanga 1 1 Lushoto 251 Mwangoi 4 Mlesa 41251004 1 2398 4 Tanga 1 1 Lushoto 261 Shume 4 Manolo 41261004 1 10598 4 Tanga 1 1 Lushoto 271 Malindi 2 Mnadani 41271002 1 5141 4 Tanga 1 1 Lushoto 281 Hemtoye 1 Hemtoye 41281001 1 2880 4 Tanga 1 1 Lushoto 291 Malibwi 1 Kwekanga 41291001 1 3451 4 Tanga 1 1 Lushoto 291 Malibwi 6 Mziragembei 41291006 1 4197 4 Tanga 1 1 Lushoto 301 Mlola 2 Lwandai 41301002 1 3463 4 Tanga 1 1 Lushoto 311 Makanya 4 Mavului 41311004 1 1706 4 Tanga 2 2 Korogwe 11 Mashewa 2 Mtini Bombo 42011002 1 532 4 Tanga 2 2 Korogwe 11 Mashewa 6 Kulasi Estate 42011006 1 1831 4 Tanga 2 2 Korogwe 21 Kizara 4 Kilangangua 42021004 1 599 63 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 4 Tanga 2 2 Korogwe 31 Magoma 1 Makangara - Mianzini,Songea,Mt 42031001 1 3228 4 Tanga 2 2 Korogwe 31 Magoma 5 Kwemazandu 42031005 1 1951 4 Tanga 2 2 Korogwe 51 Kwamndolwa 2 Mahenge - Gounguza 42051002 1 2508 4 Tanga 2 2 Korogwe 61 Kwagunda 4 Kwagonda 42061004 1 2789 4 Tanga 2 2 Korogwe 73 Mnyuzi 4 Lusanga (Station) 42073004 1 952 4 Tanga 2 2 Korogwe 83 Korogwe 1 Kwakombo 42083001 1 3376 4 Tanga 2 2 Korogwe 91 Ngombezi 1 Kitifu 42091001 1 559 4 Tanga 2 2 Korogwe 101 Msambiazi 2 Mtonga - Kwamkole Juu,Kwamkole 42101002 1 3897 4 Tanga 2 2 Korogwe 111 Vugiri 2 Kwashemshi 42111002 1 2956 4 Tanga 2 2 Korogwe 111 Vugiri 7 Old Ambangulu 42111007 1 1231 4 Tanga 2 2 Korogwe 121 Dindira 2 Mali - Shafika,Tuliani,Mbugui 42121002 1 1938 4 Tanga 2 2 Korogwe 121 Dindira 9 Manka 42121009 1 2269 4 Tanga 2 2 Korogwe 131 Bungu 2 Mlungui 42131002 1 1906 4 Tanga 2 2 Korogwe 131 Bungu 6 Bungu Msiga 42131006 1 1934 4 Tanga 2 2 Korogwe 141 Lutindi 5 Welei 42141005 1 2380 4 Tanga 2 2 Korogwe 151 Makuyuni 3 Kwasunga A 42151003 1 1716 4 Tanga 2 2 Korogwe 151 Makuyuni 8 Rutuba 42151008 1 1504 4 Tanga 2 2 Korogwe 151 Makuyuni 12 Mwenga 42151012 1 1957 4 Tanga 2 2 Korogwe 161 Chekelei 5 Chepete 42161005 1 500 4 Tanga 2 2 Korogwe 173 Mombo 2 Mwisho wa shamba 42173002 1 2879 4 Tanga 2 2 Korogwe 181 Mkalamo 5 Makole - Misajini,Shule,Majeng 42181005 1 1191 4 Tanga 2 2 Korogwe 193 Mazinde 2 Magila (Makaka) 42193002 1 3946 4 Tanga 2 2 Korogwe 193 Mazinde 4 Ngua 42193004 1 2465 4 Tanga 2 2 Korogwe 201 Mkomazi 2 Mkomazi 42201002 1 2116 4 Tanga 3 3 Muheza 11 Kilulu 3 Semngano 43011003 0 1245 4 Tanga 3 3 Muheza 11 Kilulu 4 Kilulu/Mfenesini,A,B & C 43011004 1 1835 4 Tanga 3 3 Muheza 21 Mkuzi 3 Mafere - Mchangani 43021003 1 1845 4 Tanga 3 3 Muheza 31 Mtindiro 2 Maduma 43031002 1 1814 4 Tanga 3 3 Muheza 31 Mtindiro 5 Mtindiro 43031005 1 2512 4 Tanga 3 3 Muheza 43 Lusanga 4 Mpapayu 43043004 0 938 4 Tanga 3 3 Muheza 43 Lusanga 6 Mamboleo 43043006 1 971 4 Tanga 3 3 Muheza 61 Magila 1 Magila 43061001 1 1890 4 Tanga 3 3 Muheza 81 Kisiwani 1 Mlesa 43081001 1 2759 4 Tanga 3 3 Muheza 81 Kisiwani 2 Mkwinini 43081002 0 747 4 Tanga 3 3 Muheza 81 Kisiwani 4 Kisiwani 43081004 1 1536 4 Tanga 3 3 Muheza 81 Kisiwani 7 Kwemdimu 43081007 1 1520 64 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 4 Tanga 3 3 Muheza 91 Misalai 7 Sakale 43091007 1 2061 4 Tanga 3 3 Muheza 141 Ngomeni 2 Ngomeni Station 43141002 1 3168 4 Tanga 3 3 Muheza 151 Kigombe 1 Bago Sisal Estate 43151001 0 2899 4 Tanga 3 3 Muheza 151 Kigombe 2 Kigombe Magharibi 43151002 1 2713 4 Tanga 3 3 Muheza 161 Pande 3 Mlingano 43161003 1 3350 4 Tanga 3 3 Muheza 191 Songa 1 Songa Batini 43191001 1 2178 4 Tanga 3 3 Muheza 191 Songa 3 Kilongo - Mbuyuni,Kitopeni A & 43191003 0 1604 4 Tanga 3 3 Muheza 191 Songa 4 Bwitini 43191004 1 1895 4 Tanga 3 3 Muheza 201 Potwe 2 Kimbo 43201002 1 690 4 Tanga 3 3 Muheza 301 Misozwe 4 Mwarimba 43301004 1 538 4 Tanga 3 3 Muheza 331 Zirai 1 Zirai 43331001 1 952 4 Tanga 3 3 Muheza 331 Zirai 4 Kizerui 43331004 0 2113 4 Tanga 3 3 Muheza 341 Kwafungo 1 makole - Golemazi 43341001 1 1670 4 Tanga 3 3 Muheza 341 Kwafungo 5 Mandera - Kilole,Mabovu 43341005 1 1267 4 Tanga 3 3 Muheza 351 Tingeni 4 Mpakani 43351004 1 1159 4 Tanga 4 4 Tanga 103 Mzingani 3 Mnyanjani 44103003 1 583 4 Tanga 4 4 Tanga 103 Mzingani 5 Kwanjeka Nyota 44103005 1 2671 4 Tanga 4 4 Tanga 103 Mzingani 6 Gezaulole 44103006 1 1869 4 Tanga 4 4 Tanga 103 Mzingani 8 Kwanjeka Majengo 44103008 1 2058 4 Tanga 4 4 Tanga 133 Tangasisi 2 Mwahako 44133002 1 1578 4 Tanga 4 4 Tanga 133 Tangasisi 3 Machui 44133003 1 1501 4 Tanga 4 4 Tanga 133 Tangasisi 4 Masiwani 44133004 1 1294 4 Tanga 4 4 Tanga 151 Tongoni 2 Tongoni & Saadani 44151002 1 1566 4 Tanga 4 4 Tanga 151 Tongoni 4 Mgwisha/Kaduka 44151004 1 545 4 Tanga 4 4 Tanga 161 Marungu 1 Marungu B 44161001 1 1815 4 Tanga 4 4 Tanga 173 Pongwe 1 Maranzara 44173001 1 744 4 Tanga 4 4 Tanga 173 Pongwe 2 Kisimatui 44173002 1 1524 4 Tanga 4 4 Tanga 181 Maweni 1 Kichangani/Urowa 44181001 1 7631 4 Tanga 4 4 Tanga 193 Duga 1 Magomeni A 44193001 1 3376 4 Tanga 4 4 Tanga 193 Duga 2 Mjimwema 44193002 1 1641 4 Tanga 4 4 Tanga 211 Mabokweni 1 Mabokweni,Songea,Mza mbarauni 44211001 1 1877 4 Tanga 4 4 Tanga 211 Mabokweni 2 Kiruku 44211002 1 2426 4 Tanga 4 4 Tanga 221 Kirare 1 Tundaua/Mashine 44221001 1 1640 4 Tanga 4 4 Tanga 221 Kirare 2 Majengo Mapojoni 44221002 1 1641 4 Tanga 4 4 Tanga 231 Kiomoni 1 Kiomoni 44231001 1 1060 4 Tanga 4 4 Tanga 231 Kiomoni 2 Pande B 44231002 1 2188 4 Tanga 4 4 Tanga 231 Kiomoni 3 Pande A 44231003 1 2606 4 Tanga 4 4 Tanga 241 Chongoleani 1 Chongoleani 44241001 1 1923 4 Tanga 4 4 Tanga 241 Chongoleani 3 Mpirani 44241003 1 988 65 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 4 Tanga 5 5 Pangani 31 Bweni 1 Bweni 45031001 1 1190 4 Tanga 5 5 Pangani 41 Madanga 1 Mwembeni 45041001 1 1028 4 Tanga 5 5 Pangani 41 Madanga 2 Jaira 45041002 1 592 4 Tanga 5 5 Pangani 41 Madanga 3 Madanga 45041003 1 1476 4 Tanga 5 5 Pangani 51 Kimang'a 1 Boza 45051001 1 1347 4 Tanga 5 5 Pangani 51 Kimang'a 2 Kimang'a "A" 45051002 1 1627 4 Tanga 5 5 Pangani 61 Bushiri 1 Mivumoni 45061001 1 665 4 Tanga 5 5 Pangani 61 Bushiri 2 Msaraza 45061002 1 1284 4 Tanga 5 5 Pangani 61 Bushiri 3 Kigurusimba 45061003 1 1342 4 Tanga 5 5 Pangani 61 Bushiri 4 Masaika 45061004 1 1057 4 Tanga 5 5 Pangani 71 Mwera 1 Mwera 45071001 1 2934 4 Tanga 5 5 Pangani 81 Tungamaa 1 Langoni 45081001 1 666 4 Tanga 5 5 Pangani 81 Tungamaa 2 Tungamaa 45081002 1 1359 4 Tanga 5 5 Pangani 91 Kipumbwi 1 Kwakibuyu 45091001 1 2733 4 Tanga 5 5 Pangani 91 Kipumbwi 2 Kipumbwi 45091002 1 1391 4 Tanga 5 5 Pangani 101 Mikinguni 1 Mtango 45101001 1 930 4 Tanga 5 5 Pangani 101 Mikinguni 2 Stahabu 45101002 1 1560 4 Tanga 5 5 Pangani 101 Mikinguni 4 Mtonga 45101004 1 747 4 Tanga 5 5 Pangani 111 Ubangaa 2 Mseko 45111002 1 579 4 Tanga 5 5 Pangani 121 Mkwaja 1 Sange 45121001 1 1569 4 Tanga 5 5 Pangani 121 Mkwaja 2 Mikocheni 45121002 1 1280 4 Tanga 5 5 Pangani 121 Mkwaja 3 Mkwaja 45121003 1 746 4 Tanga 5 5 Pangani 131 Mkalamo 1 Mkalamo 45131001 1 3336 4 Tanga 5 5 Pangani 131 Mkalamo 2 Mbulizaga 45131002 1 865 4 Tanga 6 6 Handeni 11 Segera 7 Michungwani 46011007 1 5593 4 Tanga 6 6 Handeni 21 Ndolwa 2 Chanika Kofi 46021002 1 2625 4 Tanga 6 6 Handeni 21 Ndolwa 5 Komkole 46021005 1 3830 4 Tanga 6 6 Handeni 31 Mazingara 1 Suwa 46031001 1 4558 4 Tanga 6 6 Handeni 41 Kwamsisi 1 Pozo 46041001 1 1128 4 Tanga 6 6 Handeni 41 Kwamsisi 3 Kwedikabu 46041003 1 2192 4 Tanga 6 6 Handeni 51 Kwasunga 3 Kwasunga 46051003 1 2748 4 Tanga 6 6 Handeni 61 Kwaluguru 3 Kwamagome 46061003 1 3048 4 Tanga 6 6 Handeni 71 Kang'ata 1 Kwaluwala 46071001 1 2153 4 Tanga 6 6 Handeni 81 Kwankonje 6 Mparagwe 46081006 1 1256 4 Tanga 6 6 Handeni 93 Vibaoni 1 Konje 46093001 1 1428 4 Tanga 6 6 Handeni 93 Vibaoni 5 Kideleko 46093005 1 3653 4 Tanga 6 6 Handeni 101 Sindeni 2 Kwamkono 46101002 1 2908 4 Tanga 6 6 Handeni 101 Sindeni 5 Sindeni 46101005 1 3157 4 Tanga 6 6 Handeni 111 Misima 4 Misima 46111004 1 3435 4 Tanga 6 6 Handeni 121 Kiva 1 Kweditibile 46121001 1 3477 4 Tanga 6 6 Handeni 131 Kabuku 3 Kabuku Kaskazini 46131003 1 2004 66 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 4 Tanga 6 6 Handeni 141 Kwamatuku 1 Nkale 46141001 1 896 4 Tanga 6 6 Handeni 151 Kwedizinga 6 Kwadoya 46151006 1 2047 4 Tanga 6 6 Handeni 161 Mgambo 2 Kabuku Ndani 46161002 1 1670 4 Tanga 6 6 Handeni 161 Mgambo 4 Komsanga 46161004 1 3525 4 Tanga 6 6 Handeni 171 Komkonga 3 Kwamachalima 46171003 1 1948 4 Tanga 6 6 Handeni 181 Mkata 1 Kwengahu 46181001 1 2085 4 Tanga 6 6 Handeni 181 Mkata 4 Mkata Magharibi 46181004 1 5827 4 Tanga 6 6 Handeni 181 Mkata 6 Manga 46181006 1 3189 4 Tanga 6 6 Handeni 193 Chanika 2 Kwenjugo Magharibi 46193002 1 3918 4 Tanga 6 6 Handeni 193 Chanika 5 Kilimilang'ombe 46193005 1 1555 4 Tanga 7 7 Kilindi 11 Lwande 1 Iwande 47011001 1 3393 4 Tanga 7 7 Kilindi 11 Lwande 3 Kwekivu 47011003 1 2892 4 Tanga 7 7 Kilindi 21 Kikunde 2 Ludewa 47021002 1 1440 4 Tanga 7 7 Kilindi 21 Kikunde 4 Tunguli 47021004 1 3128 4 Tanga 7 7 Kilindi 31 Songe 2 Kwastemba 47031002 1 2199 4 Tanga 7 7 Kilindi 31 Songe 4 Songe 47031004 1 3848 4 Tanga 7 7 Kilindi 41 Pagwi 1 Pagwi 47041001 1 2903 4 Tanga 7 7 Kilindi 41 Pagwi 4 Nyamaleni 47041004 1 2280 4 Tanga 7 7 Kilindi 51 Masagalu 2 Masagalu 47051002 1 2312 4 Tanga 7 7 Kilindi 61 Kimbe 1 Kweisapo 47061001 1 1866 4 Tanga 7 7 Kilindi 71 Kilindi 1 Misufini 47071001 1 1286 4 Tanga 7 7 Kilindi 71 Kilindi 4 Kilindi 47071004 1 4077 4 Tanga 7 7 Kilindi 81 Negero 2 Kwaluguru 47081002 1 1314 4 Tanga 7 7 Kilindi 91 Mkindi 2 Mkindi 47091002 1 3241 4 Tanga 7 7 Kilindi 101 Mvungwe 1 Kibirashi 47101001 1 4405 4 Tanga 7 7 Kilindi 101 Mvungwe 2 Gitu 47101002 1 1591 4 Tanga 7 7 Kilindi 101 Mvungwe 4 Gombero 47101004 1 4691 4 Tanga 7 7 Kilindi 101 Mvungwe 6 Mafisa Majengo 47101006 1 4666 4 Tanga 7 7 Kilindi 101 Mvungwe 7 Kwamwande 47101007 1 2961 4 Tanga 7 7 Kilindi 111 Kwediboma 2 Mzinga 47111002 1 1757 4 Tanga 7 7 Kilindi 111 Kwediboma 4 Kwediboma 47111004 1 5667 4 Tanga 7 7 Kilindi 111 Kwediboma 5 Mpalahala 47111005 1 2033 4 Tanga 7 7 Kilindi 131 Jaila 2 Mafuleta 47131002 1 1958 4 Tanga 7 7 Kilindi 131 Jaila 4 Kolang'a 47131004 1 2391 4 Tanga 7 7 Kilindi 141 Msanja 3 Mkonde 47141003 1 1567 4 Tanga 7 7 Kilindi 141 Msanja 5 Mswaki 47141005 1 2370 4 Tanga 7 7 Kilindi 151 Kisangasa 2 Mgera 47151002 1 2067 4 Tanga 3 8 Mkinga 103 Maramba 3 Mtakuja - Lugongo Sisal Estate 48103003 1 2693 4 Tanga 3 8 Mkinga 103 Maramba 5 Mapatano 48103005 0 1952 4 Tanga 3 8 Mkinga 103 Maramba 6 Mbambakofi 48103006 1 1286 67 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 4 Tanga 3 8 Mkinga 103 Maramba 10 Bantu 48103010 1 1098 4 Tanga 3 8 Mkinga 111 Daluni 1 Daluni Kisiwani 48111001 0 2089 4 Tanga 3 8 Mkinga 111 Daluni 2 Kibaoni/Bombo Darajani 48111002 1 3384 4 Tanga 3 8 Mkinga 121 Kigongoi 3 Kwekuyu 48121003 1 1510 4 Tanga 3 8 Mkinga 131 Gombero 1 Gombero 48131001 0 1464 4 Tanga 3 8 Mkinga 131 Gombero 3 Kwangena - Bamba Estate 48131003 1 937 4 Tanga 3 8 Mkinga 131 Gombero 7 Mazola - Kwa Wamasai 48131007 0 1290 4 Tanga 3 8 Mkinga 131 Gombero 8 machimboni 48131008 1 1519 4 Tanga 3 8 Mkinga 211 Mkinga 3 Magaoni 48211003 1 788 4 Tanga 3 8 Mkinga 211 Mkinga 5 Magodi - Kipumbwi,Kibaoni 48211005 0 991 4 Tanga 3 8 Mkinga 221 Duga 2 Maforoni 48221002 1 2705 4 Tanga 3 8 Mkinga 221 Duga 6 Kilulu - Magaoni, 48221006 0 984 4 Tanga 3 8 Mkinga 221 Duga 9 Mwakikonge 48221009 1 1395 4 Tanga 3 8 Mkinga 231 Mwakijembe 1 Mwakijembe 48231001 0 1927 4 Tanga 3 8 Mkinga 241 Kwale 3 Kwale 48241003 0 870 4 Tanga 3 8 Mkinga 251 Mtimbwani 4 Mtibwani 48251004 1 1826 4 Tanga 3 8 Mkinga 251 Mtimbwani 5 Msambiazi 48251005 0 316 4 Tanga 3 8 Mkinga 261 Moa 4 Mbuluni/Zungibari 48261004 0 1177 4 Tanga 3 8 Mkinga 261 Moa 5 Vuo 48261005 1 1045 4 Tanga 3 8 Mkinga 311 Manza 3 Manza - Sigaya,Vibandani,Manza 48311003 0 1716 4 Tanga 3 8 Mkinga 321 Mhinduro 1 Matemboni/Majengo,Mat umbuli 48321001 1 811 4 Tanga 3 8 Mkinga 321 Mhinduro 2 Mhinduro 48321002 0 1686 4 Tanga 3 8 Mkinga 321 Mhinduro 5 Churwa 48321005 1 2344 4 Tanga 3 8 Mkinga 321 Mhinduro 7 Bosha 48321007 0 2616 5 Morogoro 1 1 Kilosa 11 Chakwale 2 Idibo 51011002 1 4776 5 Morogoro 1 1 Kilosa 11 Chakwale 6 Ndogoni- Kinangali,Maweni&Mage n 51011006 1 2829 5 Morogoro 1 1 Kilosa 21 Iyogwe 4 Italagwe-Diora,Bungoma &Matale 51021004 1 3581 5 Morogoro 1 1 Kilosa 31 Berega 4 Kiegea-Miembeni /Nyanja Stop 51031004 1 3789 5 Morogoro 1 1 Kilosa 43 Magubike 3 Maguha-Inyunywe 51043003 1 2668 5 Morogoro 1 1 Kilosa 51 Mamboya 5 Kitange II-Visandu 51051005 1 3956 5 Morogoro 1 1 Kilosa 63 Dumila 3 Kwambe 51063003 1 958 5 Morogoro 1 1 Kilosa 73 Magole 3 Mbuyuni (Mbigili) 51073003 1 4695 5 Morogoro 1 1 Kilosa 83 Msowero 5 Mvumi 51083005 1 6417 68 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 5 Morogoro 1 1 Kilosa 101 Chanzuru 1 Mkata Station 51101001 1 715 5 Morogoro 1 1 Kilosa 101 Chanzuru 5 Ilonga 51101005 1 4606 5 Morogoro 1 1 Kilosa 171 Mabwerebwere 6 Malangali 51171006 1 2485 5 Morogoro 1 1 Kilosa 171 Mabwerebwere 8 Malui 51171008 1 3919 5 Morogoro 1 1 Kilosa 201 Ruhembe 2 Kidogobasi 51201002 1 3484 5 Morogoro 1 1 Kilosa 213 Kidodi 2 Msowero 51213002 1 2081 5 Morogoro 1 1 Kilosa 213 Kidodi 5 Kifinga -Mhovu 51213005 1 4206 5 Morogoro 1 1 Kilosa 231 Malolo 3 Malolo 'A' 51231003 1 2748 5 Morogoro 1 1 Kilosa 243 Kisanga 2 Msolwa 51243002 1 4082 5 Morogoro 1 1 Kilosa 261 Ulaya 5 Mhenda 51261005 1 3163 5 Morogoro 1 1 Kilosa 271 Zombo 3 Madudumizi 51271003 1 3637 5 Morogoro 1 1 Kilosa 291 Masanze 2 Changarawe -Madizini 51291002 1 2576 5 Morogoro 1 1 Kilosa 301 Kidete 2 Mzaganza 51301002 1 1207 5 Morogoro 1 1 Kilosa 321 Chanjale 5 Lukando 51321005 1 971 5 Morogoro 1 1 Kilosa 331 Chagongwe 2 Chagongwe - Chinangali,Ndete 51331002 1 2745 5 Morogoro 1 1 Kilosa 351 Rubeho 3 Kisitwi/Manyemba 51351003 1 4200 5 Morogoro 1 1 Kilosa 363 Gairo 2 Luhwaji 51363002 1 2343 5 Morogoro 1 1 Kilosa 371 Kibedya 3 Tabu hotel 51371003 1 1769 5 Morogoro 2 2 Morogoro 11 Kasanga 4 Longwe 52011004 1 701 5 Morogoro 2 2 Morogoro 21 Kolero 1 Lukange -Tendegela 52021001 1 1811 5 Morogoro 2 2 Morogoro 21 Kolero 6 Mlagano -Tambuu & Kikangazi 52021006 1 1091 5 Morogoro 2 2 Morogoro 31 Mvuha 5 Kongwa -Barabarani 52031005 1 2421 5 Morogoro 2 2 Morogoro 41 Selembala 4 Bwila Chini - Kwamnambala,Kwatu 52041004 1 1372 5 Morogoro 2 2 Morogoro 51 Bwakila Chini 3 Bonye -Mtambani,Balawa & Mkesa 52051003 1 4241 5 Morogoro 2 2 Morogoro 61 Bwakila Juu 1 Bwakila Juu-Kangazi & Mondo 52061001 1 2868 5 Morogoro 2 2 Morogoro 71 Kisaki 2 Gomero-Kimala 52071002 1 4480 5 Morogoro 2 2 Morogoro 91 Singisa 2 Lumba Chini - Kubungu,Tendegela 52091002 1 4782 5 Morogoro 2 2 Morogoro 91 Singisa 6 Singisa -Ziwa,Lazanga & Singis 52091006 1 1978 5 Morogoro 2 2 Morogoro 101 Mkambalani 4 Mkambalani -Taasisi (Kingulwira 52101004 1 4623 5 Morogoro 2 2 Morogoro 111 Mikese 2 Lubungo - Mpakani,Lubungo mjini 52111002 1 2451 5 Morogoro 2 2 Morogoro 111 Mikese 5 Mikese Station -Diora 52111005 1 3436 69 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 5 Morogoro 2 2 Morogoro 121 Kidugalo 6 Seregete 'B' - Ng'hese,Kudiguzi 52121006 1 1011 5 Morogoro 2 2 Morogoro 143 Ngerengere 1 Sinyaulime-Dihola- Magoza(Kipar 52143001 1 2168 5 Morogoro 2 2 Morogoro 143 Ngerengere 7 Kiwege-Boya-Kitega 52143007 1 949 5 Morogoro 2 2 Morogoro 161 Kinole 1 Tandai -Lukenge 52161001 1 4219 5 Morogoro 2 2 Morogoro 171 Kiroka 3 Kikundi- Ziwatanzi,Lukangazi 52171003 1 5106 5 Morogoro 2 2 Morogoro 181 Mkuyuni 1 Kibuko -Midilu 52181001 1 1960 5 Morogoro 2 2 Morogoro 181 Mkuyuni 4 Mkuyuni- (Misala,Mkuyuni'A'Mafu 52181004 1 4156 5 Morogoro 2 2 Morogoro 181 Mkuyuni 7 Luholole-Kibambawe 52181007 1 3407 5 Morogoro 2 2 Morogoro 201 Kibogwa 1 Kifulu- (Shuleni,CCM,Dabala) 52201001 1 633 5 Morogoro 2 2 Morogoro 201 Kibogwa 5 Kirunga-(Situa,Talani & Mwembe 52201005 1 1310 5 Morogoro 2 2 Morogoro 223 Kisemu 2 Mlono-(mlono & Lundi Juu) 52223002 1 1625 5 Morogoro 2 2 Morogoro 223 Kisemu 6 Kibangile-Kinasimba 52223006 1 1607 5 Morogoro 2 2 Morogoro 231 Lundi 3 Lundi - Lundi 52231003 1 2490 5 Morogoro 2 2 Morogoro 251 Tawa 3 Logo -Mlimbo & Lunguli 52251003 1 1113 5 Morogoro 3 3 Kilombero 13 Kidatu 1 Kidatu-Kidatu 'B' 53013001 1 6903 5 Morogoro 3 3 Kilombero 13 Kidatu 2 Msolwa Station-Nyange 53013002 1 9834 5 Morogoro 3 3 Kilombero 21 Sanje 2 Msolwa Ujamaa - Malamato & Nyum 53021002 1 4490 5 Morogoro 3 3 Kilombero 31 Mkula 4 Katurukila -Tanzania 53031004 1 2582 5 Morogoro 3 3 Kilombero 43 Mang'ula 2 Kanyenja 53043002 1 2074 5 Morogoro 3 3 Kilombero 43 Mang'ula 5 Mikoleko -Mission'A' 53043005 1 2329 5 Morogoro 3 3 Kilombero 51 Kisawasawa 2 Kanolo 53051002 1 1128 5 Morogoro 3 3 Kilombero 61 Kiberege 1 Mkasu -Namisata 53061001 1 2914 5 Morogoro 3 3 Kilombero 61 Kiberege 2 Kiberege -TAZARA 53061002 1 8996 5 Morogoro 3 3 Kilombero 73 Kibaoni 2 Kilama 53073002 1 1654 5 Morogoro 3 3 Kilombero 73 Kibaoni 5 Mbasa 53073005 1 5463 5 Morogoro 3 3 Kilombero 91 Lumelo 1 Lumelo 53091001 1 5233 5 Morogoro 3 3 Kilombero 91 Lumelo 4 Michenga 53091004 1 3632 5 Morogoro 3 3 Kilombero 101 Idete 1 Idete 53101001 1 5720 5 Morogoro 3 3 Kilombero 101 Idete 3 Namwawala 53101003 1 4827 5 Morogoro 3 3 Kilombero 111 Mbingu 2 Mbingu 53111002 1 6248 5 Morogoro 3 3 Kilombero 121 Mofu 1 Mofu 53121001 1 3189 5 Morogoro 3 3 Kilombero 131 Mchombe 2 Mchombe 53131002 1 5916 5 Morogoro 3 3 Kilombero 131 Mchombe 4 Mngeta 53131004 1 4839 70 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 5 Morogoro 3 3 Kilombero 131 Mchombe 6 Ikule 53131006 1 3535 5 Morogoro 3 3 Kilombero 141 Chita 3 Chita 53141003 1 8803 5 Morogoro 3 3 Kilombero 141 Chita 4 Chita 53141004 1 3573 5 Morogoro 3 3 Kilombero 163 Mlimba 1 Katengakelu 53163001 1 6381 5 Morogoro 3 3 Kilombero 163 Mlimba 2 Msolwa 53163002 1 1484 5 Morogoro 3 3 Kilombero 163 Mlimba 4 Viwanja Sitini 53163004 1 4963 5 Morogoro 3 3 Kilombero 171 Utengule 1 Mpanga 53171001 1 2615 5 Morogoro 3 3 Kilombero 181 Masagati 3 Taweta 53181003 1 2469 5 Morogoro 4 4 Ulanga 11 Minepa 2 Minepa -Alabama & Matinge 54011002 1 2096 5 Morogoro 4 4 Ulanga 23 Lupiro 2 Igumbiro -Kikuyu & Igumbiro 54023002 1 2279 5 Morogoro 4 4 Ulanga 31 Kichangani 1 Kichangani Hospital 54031001 1 3163 5 Morogoro 4 4 Ulanga 41 Msogezi 1 Msogezi 54041001 1 2945 5 Morogoro 4 4 Ulanga 53 Vigoi 2 Mbagula -Mbagula Juu & Chini 54053002 1 1125 5 Morogoro 4 4 Ulanga 53 Vigoi 5 Nawenge -Ngongua 54053005 1 3075 5 Morogoro 4 4 Ulanga 71 Isongo 2 Isongo -Kolowa & Isongo Kati 54071002 1 4109 5 Morogoro 4 4 Ulanga 81 Ruaha 2 Ruaha 54081002 1 4992 5 Morogoro 4 4 Ulanga 81 Ruaha 3 Mgolo 54081003 1 1577 5 Morogoro 4 4 Ulanga 91 Chirombola 2 Mzelezi 54091002 1 2388 5 Morogoro 4 4 Ulanga 111 Euga 1 Euga 54111001 1 1440 5 Morogoro 4 4 Ulanga 123 Mwaya 2 Mwaya 54123002 1 1770 5 Morogoro 4 4 Ulanga 141 Mbuga 1 Mbuga 54141001 1 2833 5 Morogoro 4 4 Ulanga 151 Ilonga 1 Chigandugandu 54151001 1 3978 5 Morogoro 4 4 Ulanga 151 Ilonga 2 Luhombero 54151002 1 4015 5 Morogoro 4 4 Ulanga 161 Kilosa Mpepo 1 Ihowanja 54161001 1 1931 5 Morogoro 4 4 Ulanga 171 Ngoheranga 2 Ngoheranga 54171002 1 2183 5 Morogoro 4 4 Ulanga 193 Malinyi 1 Igawa 54193001 1 4229 5 Morogoro 4 4 Ulanga 193 Malinyi 2 Misegese 54193002 1 4984 5 Morogoro 4 4 Ulanga 193 Malinyi 4 Kipingo 54193004 1 5471 5 Morogoro 4 4 Ulanga 201 Sofi 2 Majiji 54201002 1 3496 5 Morogoro 4 4 Ulanga 211 Usangule 1 Usangule -Mipululu 54211001 1 5370 5 Morogoro 4 4 Ulanga 211 Usangule 2 Kalangakelo - Tondo,Kuluweka & 54211002 1 3960 5 Morogoro 4 4 Ulanga 223 Mtimbira 1 Madibira 54223001 1 2842 5 Morogoro 4 4 Ulanga 231 Itete 1 Minazini -Madabadaba 54231001 1 7823 5 Morogoro 4 4 Ulanga 231 Itete 2 Njiwa -Ipera 54231002 1 7557 5 Morogoro 4 4 Ulanga 241 Iragua 2 Iragua -Igunda 54241002 1 4550 5 Morogoro 5 5 Morogoro ( 93 Mazimbu 1 Madanganya 55093001 1 426 71 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 5 Morogoro 5 5 Morogoro ( 93 Mazimbu 2 Mindu 55093002 1 920 5 Morogoro 5 5 Morogoro ( 93 Mazimbu 3 Kasanga 55093003 1 1028 5 Morogoro 5 5 Morogoro ( 93 Mazimbu 4 Lugala 55093004 1 436 5 Morogoro 5 5 Morogoro ( 143 Mlimani 1 Choma/Mbete,Rufuza 55143001 1 2422 5 Morogoro 5 5 Morogoro ( 143 Mlimani 2 Turo - Ruvuma,Kisosa 55143002 1 665 5 Morogoro 5 5 Morogoro ( 163 Kingolwira 1 Kung'wa 55163001 1 399 5 Morogoro 5 5 Morogoro ( 163 Kingolwira 2 Ng'ong'olo 55163002 1 441 5 Morogoro 5 5 Morogoro ( 173 Bigwa 1 Mungi 55173001 1 474 5 Morogoro 5 5 Morogoro ( 173 Bigwa 2 Korogoso 55173002 1 248 5 Morogoro 5 5 Morogoro ( 173 Bigwa 3 Vituli 55173003 1 713 5 Morogoro 5 5 Morogoro ( 173 Bigwa 4 Bohomera 55173004 1 385 5 Morogoro 5 5 Morogoro ( 181 Mzinga 1 Mambani 55181001 1 574 5 Morogoro 5 5 Morogoro ( 181 Mzinga 2 Kilala 55181002 1 267 5 Morogoro 5 5 Morogoro ( 181 Mzinga 3 Mundu - Luhungo 55181003 1 467 5 Morogoro 5 5 Morogoro ( 181 Mzinga 4 Kivaza 55181004 1 497 5 Morogoro 5 5 Morogoro ( 181 Mzinga 5 Tindigo & Mfine 55181005 1 878 5 Morogoro 5 5 Morogoro ( 181 Mzinga 6 Konga 55181006 1 1047 5 Morogoro 5 5 Morogoro ( 181 Mzinga 7 Kauzeni 55181007 1 1052 5 Morogoro 5 5 Morogoro ( 193 Kihonda 1 Kiegea 'B' 55193001 1 539 5 Morogoro 5 5 Morogoro ( 193 Kihonda 2 Ngerengere 55193002 1 392 5 Morogoro 5 5 Morogoro ( 193 Kihonda 3 Kipera,Kiegea,CCT forest,Nguvu 55193003 1 351 5 Morogoro 5 5 Morogoro ( 193 Kihonda 4 Kiegea 'A' 55193004 1 291 5 Morogoro 5 5 Morogoro ( 193 Kihonda 5 Lukobe Chini 55193005 1 820 5 Morogoro 5 5 Morogoro ( 193 Kihonda 6 Lukobe Juu 55193006 1 432 5 Morogoro 6 6 Mvomero 13 Mvomero 3 Dibamba-Kongowe 56013003 1 896 5 Morogoro 6 6 Mvomero 13 Mvomero 7 Makuyu-Mkocheni 56013007 1 3598 5 Morogoro 6 6 Mvomero 21 Hembeti 1 Hembeti -Miembe Kumi 56021001 1 3803 5 Morogoro 6 6 Mvomero 21 Hembeti 3 Dihombo -CCM 56021003 1 2545 5 Morogoro 6 6 Mvomero 31 Maskati 2 Dibago 56031002 1 1905 5 Morogoro 6 6 Mvomero 41 Kibati 1 Diburuma 56041001 1 1583 5 Morogoro 6 6 Mvomero 41 Kibati 5 Pemba 56041005 1 4244 5 Morogoro 6 6 Mvomero 41 Kibati 7 Salawe 56041007 1 4663 5 Morogoro 6 6 Mvomero 51 Sungaji 3 Mbogo -Mkwajuni & Mhuvuge 56051003 1 3039 5 Morogoro 6 6 Mvomero 63 Mhonda 1 Kweli Kwiji -Luamba Juu & Chin 56063001 1 2619 5 Morogoro 6 6 Mvomero 71 Diongoya 1 Lusanga -Mkuyuni 56071001 1 5820 5 Morogoro 6 6 Mvomero 71 Diongoya 3 Digoma -Bandabichi & Kwaputu 56071003 1 2847 5 Morogoro 6 6 Mvomero 83 Mtibwa 3 Kidudwe-Kwasungura 56083003 1 5389 72 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 5 Morogoro 6 6 Mvomero 83 Mtibwa 6 Mlumbilo - Kichangani 56083006 1 2045 5 Morogoro 6 6 Mvomero 91 Kanga 3 Difinga 56091003 1 1892 5 Morogoro 6 6 Mvomero 101 Bunduki 1 Tandari -Luhuga,Msani & Nyambu 56101001 1 1348 5 Morogoro 6 6 Mvomero 111 Kikeo 1 Kikeo -Mtamba 56111001 1 2152 5 Morogoro 6 6 Mvomero 111 Kikeo 4 Masalawe -Ndosi,Tagata &Lufune 56111004 1 1773 5 Morogoro 6 6 Mvomero 123 Langali 2 Lusungu - Nyamundi,Makoo,Langaz 56123002 1 1301 5 Morogoro 6 6 Mvomero 131 Tchenzema 3 Nyandira -Lubwe,Vidigisi & Nya 56131003 1 3207 5 Morogoro 6 6 Mvomero 141 Mzumbe 1 Sangasanga -Gezaulole & Masanze 56141001 1 1376 5 Morogoro 6 6 Mvomero 141 Mzumbe 5 Tangeni -Kikoya 56141005 1 5026 5 Morogoro 6 6 Mvomero 151 Mlali 2 Mlali - Vitonga 56151002 1 4201 5 Morogoro 6 6 Mvomero 151 Mlali 5 Homboza - Chohelo 56151005 1 5220 5 Morogoro 6 6 Mvomero 161 Doma 2 Doma - Doma Kilosa Stendi 56161002 1 2728 5 Morogoro 6 6 Mvomero 161 Doma 5 Kihondo -Rudia 56161005 1 908 5 Morogoro 6 6 Mvomero 171 Melela 2 Melela - Mlandizi 56171002 1 7004 6 Pwani 1 1 Bagamoyo 11 Kiwangwa 2 Kiwangwa - Zongomelo 61011002 1 8205 6 Pwani 1 1 Bagamoyo 11 Kiwangwa 3 Fukakosi 61011003 1 2806 6 Pwani 1 1 Bagamoyo 21 Msata 2 Msata - Mifugoni 61021002 1 3490 6 Pwani 1 1 Bagamoyo 31 Miono 2 Kikaro 61031002 1 4440 6 Pwani 1 1 Bagamoyo 31 Miono 5 Hondogo 61031005 1 1139 6 Pwani 1 1 Bagamoyo 31 Miono 7 Rupungwi 61031007 1 3686 6 Pwani 1 1 Bagamoyo 41 Mkange 2 Matipwili 61041002 1 2777 6 Pwani 1 1 Bagamoyo 41 Mkange 4 Mkange - Java 61041004 1 2933 6 Pwani 1 1 Bagamoyo 71 Kiromo 1 Kiromo 61071001 1 2582 6 Pwani 1 1 Bagamoyo 81 Zinga 1 Kondo 61081001 1 1557 6 Pwani 1 1 Bagamoyo 81 Zinga 4 Zinga 61081004 1 3329 6 Pwani 1 1 Bagamoyo 81 Zinga 7 Kerenge/ Matumbi 61081007 1 1394 6 Pwani 1 1 Bagamoyo 91 Yombo 4 Chasimba 61091004 1 2023 6 Pwani 1 1 Bagamoyo 101 Vigwaza 3 Visezi 61101003 1 2274 6 Pwani 1 1 Bagamoyo 101 Vigwaza 4 Vigwaza 61101004 1 3386 6 Pwani 1 1 Bagamoyo 111 Talawanda 2 Malivundo 61111002 1 846 6 Pwani 1 1 Bagamoyo 123 Chalinze 1 Mbwilingu 61123001 1 4118 6 Pwani 1 1 Bagamoyo 123 Chalinze 5 Msolwa 61123005 1 3462 6 Pwani 1 1 Bagamoyo 123 Chalinze 7 Pera 61123007 1 2075 6 Pwani 1 1 Bagamoyo 133 Lugoba 3 Mboga 61133003 1 3254 6 Pwani 1 1 Bagamoyo 133 Lugoba 7 Mindutulieni 61133007 1 1718 73 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 6 Pwani 1 1 Bagamoyo 141 Ubenazomozi 2 Kaloleni 61141002 1 2489 6 Pwani 1 1 Bagamoyo 141 Ubenazomozi 5 Visakazi 61141005 1 3313 6 Pwani 1 1 Bagamoyo 151 Mbwewe 2 Mbwewe 61151002 1 5569 6 Pwani 1 1 Bagamoyo 151 Mbwewe 4 Pongwe Kiona 61151004 1 2824 6 Pwani 1 1 Bagamoyo 151 Mbwewe 6 Kwaruhombo 61151006 1 2059 6 Pwani 2 2 Kibaha 13 Tumbi 1 Mwanalugali 62013001 1 1595 6 Pwani 2 2 Kibaha 13 Tumbi 4 Sofu 62013004 1 329 6 Pwani 2 2 Kibaha 13 Tumbi 6 Mkuza 62013006 1 2625 6 Pwani 2 2 Kibaha 13 Tumbi 9 Kidimu 62013009 1 1314 6 Pwani 2 2 Kibaha 23 Kibaha 1 Mikongeni 62023001 1 602 6 Pwani 2 2 Kibaha 23 Kibaha 4 Kongowe 62023004 1 4298 6 Pwani 2 2 Kibaha 23 Kibaha 5 Miembe 7 62023005 1 1675 6 Pwani 2 2 Kibaha 23 Kibaha 7 Mwenda Pole 62023007 1 976 6 Pwani 2 2 Kibaha 23 Kibaha 9 Kidenge 62023009 1 1366 6 Pwani 2 2 Kibaha 31 Magindu 1 Gwata - Mgaluka/Gwata 62031001 1 2441 6 Pwani 2 2 Kibaha 31 Magindu 2 Gumba - Kigoda 62031002 1 2581 6 Pwani 2 2 Kibaha 31 Magindu 3 Magindu - Lukarasi/Mnyonge 62031003 1 2540 6 Pwani 2 2 Kibaha 41 Soga 1 Vikuge 62041001 1 1510 6 Pwani 2 2 Kibaha 41 Soga 3 Mpiji 62041003 1 848 6 Pwani 2 2 Kibaha 41 Soga 4 Soga - Alavi 62041004 1 2092 6 Pwani 2 2 Kibaha 51 Visaga 1 Miswe - Chini 62051001 1 1380 6 Pwani 2 2 Kibaha 51 Visaga 3 Visiga - Madafu 62051003 1 1664 6 Pwani 2 2 Kibaha 51 Visaga 5 Zegereni 62051005 1 610 6 Pwani 2 2 Kibaha 51 Visaga 8 Zogowale 62051008 1 883 6 Pwani 2 2 Kibaha 61 Ruvu 1 Kitomondo 62061001 1 492 6 Pwani 2 2 Kibaha 61 Ruvu 3 Ruvu/Station 62061003 1 1008 6 Pwani 2 2 Kibaha 61 Ruvu 5 Lipunga 62061005 1 662 6 Pwani 2 2 Kibaha 61 Ruvu 7 Ngeta 62061007 1 991 6 Pwani 2 2 Kibaha 73 Mlandizi 1 Vikuruti 62073001 1 2048 6 Pwani 2 2 Kibaha 73 Mlandizi 2 Mlandizi 'B' - Kisabi 62073002 1 4218 6 Pwani 2 2 Kibaha 73 Mlandizi 3 Disunyala 62073003 1 1253 6 Pwani 2 2 Kibaha 81 Kwala 3 Mwembe Ngonzi 62081003 1 267 6 Pwani 3 3 Kisarawe 13 Kisarawe 2 kazimzumbwi 63013002 1 1592 6 Pwani 3 3 Kisarawe 13 Kisarawe 4 Visegese 63013004 1 966 6 Pwani 3 3 Kisarawe 21 Msimbu 2 Kitanga 63021002 1 1327 6 Pwani 3 3 Kisarawe 21 Msimbu 4 Homboza 63021004 1 1919 6 Pwani 3 3 Kisarawe 21 Msimbu 5 Msimbu 63021005 1 3498 6 Pwani 3 3 Kisarawe 21 Msimbu 6 Gumba 63021006 1 1670 6 Pwani 3 3 Kisarawe 31 Masaki 1 Masaki 63031001 1 2799 6 Pwani 3 3 Kisarawe 31 Masaki 3 Kisanga 63031003 1 1660 74 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 6 Pwani 3 3 Kisarawe 41 Kibuta 2 Mtamba 63041002 1 987 6 Pwani 3 3 Kisarawe 41 Kibuta 5 Kibuta 63041005 1 2164 6 Pwani 3 3 Kisarawe 41 Kibuta 7 Chang'ombe 'B' 63041007 1 785 6 Pwani 3 3 Kisarawe 51 Marumbo 3 Marumbo 63051003 1 1193 6 Pwani 3 3 Kisarawe 51 Marumbo 6 Kikwete 63051006 1 1137 6 Pwani 3 3 Kisarawe 63 Maneromango 3 Msegamo,Mkuyuni 63063003 1 746 6 Pwani 3 3 Kisarawe 63 Maneromango 7 Boga 'A' 63063007 1 1883 6 Pwani 3 3 Kisarawe 73 Msanga 2 Visiga 63073002 1 1061 6 Pwani 3 3 Kisarawe 81 Marui 1 Marui Mipera 63081001 1 1022 6 Pwani 3 3 Kisarawe 91 Cholesamvula 2 Kwala 63091002 1 2139 6 Pwani 3 3 Kisarawe 91 Cholesamvula 4 Yombo Lukinga 63091004 1 750 6 Pwani 3 3 Kisarawe 91 Cholesamvula 6 Mafumbi 63091006 1 534 6 Pwani 3 3 Kisarawe 101 Vikumbulu 5 Vikumbulu 63101005 1 1342 6 Pwani 3 3 Kisarawe 111 Mafinzi 3 Gwata 63111003 1 2399 6 Pwani 3 3 Kisarawe 121 Kuruhi 2 Mtakayo 63121002 1 870 6 Pwani 3 3 Kisarawe 131 Mzenga 1 Mzenga 'A' 63131001 1 1168 6 Pwani 3 3 Kisarawe 131 Mzenga 4 Mitengwe 63131004 1 2143 6 Pwani 3 3 Kisarawe 141 Vihingo 3 Kibwemwenda 63141003 1 807 6 Pwani 3 3 Kisarawe 153 Kiluvya 1 Tondoroni 63153001 1 1170 6 Coast 4 4 Mkuranga 13 Mkuranga 1 Hoyoyo 64013001 1 2546 6 Coast 4 4 Mkuranga 13 Mkuranga 2 Kologwa 64013002 1 361 6 Coast 4 4 Mkuranga 13 Mkuranga 8 Kiparang'anda 'B' 64013008 1 2031 6 Coast 4 4 Mkuranga 21 Tambani 2 Tambani 64021002 1 1246 6 Coast 4 4 Mkuranga 21 Tambani 4 Mlamleni 64021004 1 2852 6 Coast 4 4 Mkuranga 21 Tambani 7 Dondwe 64021007 1 2144 6 Coast 4 4 Mkuranga 33 Vikindu 3 Vikindu 64033003 1 2995 6 Coast 4 4 Mkuranga 33 Vikindu 5 Marogoro 64033005 1 840 6 Coast 4 4 Mkuranga 33 Vikindu 8 Vianzi 64033008 1 2768 6 Coast 4 4 Mkuranga 41 Mbezi 4 Msufini - Kidete 64041004 1 2356 6 Coast 4 4 Mkuranga 41 Mbezi 7 Msorwa 64041007 1 819 6 Coast 4 4 Mkuranga 61 Kisiju 6 Kalole 64061006 1 1903 6 Coast 4 4 Mkuranga 61 Kisiju 8 kwale Island 64061008 1 554 6 Coast 4 4 Mkuranga 71 Magawa 6 Mdimni 64071006 1 938 6 Coast 4 4 Mkuranga 81 Kitomondo 1 Kitomondo 64081001 1 2255 6 Coast 4 4 Mkuranga 81 Kitomondo 4 Miteza 64081004 1 1817 6 Coast 4 4 Mkuranga 81 Kitomondo 7 Mitaranda 64081007 1 1060 6 Coast 4 4 Mkuranga 91 Lukanga 4 Misasa 64091004 1 2413 6 Coast 4 4 Mkuranga 91 Lukanga 5 Njopeka 64091005 1 5310 6 Coast 4 4 Mkuranga 101 Nyamato 3 Mkiu 64101003 1 2893 6 Coast 4 4 Mkuranga 113 Kimanzichana 2 Kiimbwanindi 64113002 1 3654 6 Coast 4 4 Mkuranga 123 Mkamba 1 Kizomla 64123001 1 1547 75 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 6 Coast 4 4 Mkuranga 123 Mkamba 4 Lupondo 64123004 1 3484 6 Coast 4 4 Mkuranga 123 Mkamba 8 Chamgohi 64123008 1 569 6 Coast 4 4 Mkuranga 131 Panzuo 6 Mbezi 64131006 1 488 6 Coast 4 4 Mkuranga 141 Bupu 4 Bupu 64141004 1 1447 6 Coast 4 4 Mkuranga 153 Mwalusembe 3 Bigwa 64153003 1 2369 6 Coast 5 5 Rufiji 43 Utete 3 Utunge/Kindwiti 65043003 1 2061 6 Coast 5 5 Rufiji 51 Mkongo 2 Mkongo Kaskazini - Nyipala 65051002 1 1461 6 Coast 5 5 Rufiji 61 Ngorongo 1 Kilimani Mashariki - Mkunga 65061001 1 1327 6 Coast 5 5 Rufiji 61 Ngorongo 4 Ngorongo Magharibi - Kikongono 65061004 1 1026 6 Coast 5 5 Rufiji 61 Ngorongo 7 Kipugila 65061007 1 1230 6 Coast 5 5 Rufiji 71 Mwaseni 2 Mwaseni/Mibuyusaba 65071002 1 1399 6 Coast 5 5 Rufiji 83 Kibiti 2 Kimbuga 65083002 1 2484 6 Coast 5 5 Rufiji 83 Kibiti 5 Mtawanya 65083005 1 3141 6 Coast 5 5 Rufiji 83 Kibiti 7 Bumba/Msoro 65083007 1 741 6 Coast 5 5 Rufiji 93 Bungu 1 Jaribu Mpakani 65093001 1 6664 6 Coast 5 5 Rufiji 93 Bungu 2 Mjawa - Mtetani 65093002 1 1494 6 Coast 5 5 Rufiji 93 Bungu 3 Uponda-Uchembe Kusini 65093003 1 3016 6 Coast 5 5 Rufiji 93 Bungu 5 Bungu 'A' 65093005 1 3259 6 Coast 5 5 Rufiji 101 Mahege 1 Kivinja 'A' 65101001 1 1984 6 Coast 5 5 Rufiji 101 Mahege 5 Nyakinyo 65101005 1 554 6 Coast 5 5 Rufiji 111 Mchukwi 1 Mchukwi 'A' 65111001 1 2572 6 Coast 5 5 Rufiji 111 Mchukwi 3 Machipi 65111003 1 636 6 Coast 5 5 Rufiji 123 Chumbi 1 Chumbi 'C'/Magh 65123001 1 1092 6 Coast 5 5 Rufiji 123 Chumbi 4 Mohoro/Old Mohoro 65123004 1 4131 6 Coast 5 5 Rufiji 131 Mbwara 2 Mbwara Mash. 65131002 1 3041 6 Coast 5 5 Rufiji 141 Mtunda 2 Mtunda 'B' 65141002 1 2167 6 Coast 5 5 Rufiji 151 Ruaruke 2 Rungungu 65151002 1 2237 6 Coast 5 5 Rufiji 151 Ruaruke 5 Ruaruke 'B' 65151005 1 2680 6 Coast 5 5 Rufiji 161 Salale 2 Mchinga Mfisini 65161002 1 4694 6 Coast 5 5 Rufiji 171 Mbuchi 2 Mbwera Magh - Kumbacha 65171002 1 2533 6 Coast 5 5 Rufiji 181 Kiongoroni 2 Jaja/Mji mwema,Bumbwamani 65181002 1 1463 6 Coast 5 5 Rufiji 191 Maparoni 3 Kiasi/Nyanguni,Poroti 65191003 1 1932 6 Pwani 6 6 Mafia 11 Kanga 1 Bweni 66011001 1 1418 6 Pwani 6 6 Mafia 11 Kanga 2 Kanga 66011002 1 1912 6 Pwani 6 6 Mafia 21 Kirongwe 1 Jojo 66021001 1 675 6 Pwani 6 6 Mafia 21 Kirongwe 2 Banja 66021002 1 629 76 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 6 Pwani 6 6 Mafia 21 Kirongwe 3 Jimbo 66021003 1 1933 6 Pwani 6 6 Mafia 21 Kirongwe 4 Kirongwe 66021004 1 2143 6 Pwani 6 6 Mafia 31 Baleni 1 Baleni 66031001 1 2938 6 Pwani 6 6 Mafia 31 Baleni 2 Kungwi 66031002 1 2192 6 Pwani 6 6 Mafia 31 Baleni 3 Ndagoni 66031003 1 2155 6 Pwani 6 6 Mafia 31 Baleni 4 Chunguruma 66031004 1 1881 6 Pwani 6 6 Mafia 43 Kilindoni 1 Dongo 66043001 1 1735 6 Pwani 6 6 Mafia 43 Kilindoni 2 KilindoniBwejuu 66043002 1 522 6 Pwani 6 6 Mafia 51 Mibulani 1 Mlongo 66051001 1 735 6 Pwani 6 6 Mafia 51 Mibulani 2 Mibulani 66051002 1 1572 6 Pwani 6 6 Mafia 51 Mibulani 3 Chemuchemu 66051003 1 2065 6 Pwani 6 6 Mafia 61 Kiegeani 1 Malimbani 66061001 1 1177 6 Pwani 6 6 Mafia 61 Kiegeani 2 Kiegeani 66061002 1 2212 6 Pwani 6 6 Mafia 71 Jibondo 1 Chole 66071001 1 898 6 Pwani 6 6 Mafia 71 Jibondo 2 Juani 66071002 1 935 6 Pwani 6 6 Mafia 71 Jibondo 3 Jibondo 66071003 1 1580 7 Dar es Salaam 1 1 Kinondoni 133 Kibamba 1 kiluvya Kati 71133001 1 3927 7 Dar es Salaam 1 1 Kinondoni 133 Kibamba 2 Kibwegere 71133002 1 1553 7 Dar es Salaam 1 1 Kinondoni 133 Kibamba 3 Kwebe - Mloganzila 71133003 1 4302 7 Dar es Salaam 1 1 Kinondoni 141 Goba 1 Kinzudi 71141001 1 2870 7 Pwani 1 1 Bagamoyo 141 Ubenazomozi 2 Kaloleni 71141002 1 2749 7 Dar es Salaam 1 1 Kinondoni 141 Goba 3 Matosa 71141003 1 2238 7 Dar es Salaam 1 1 Kinondoni 141 Goba 4 Kulangula 71141004 1 1100 7 Dar es Salaam 1 1 Kinondoni 163 Kunduchi 1 Madala 71163001 1 3838 7 Dar es Salaam 1 1 Kinondoni 171 Mbweni 1 Mpiji 71171001 1 530 7 Dar es Salaam 1 1 Kinondoni 171 Mbweni 2 Maputo 71171002 1 790 7 Dar es Salaam 1 1 Kinondoni 171 Mbweni 3 Mbweni 71171003 1 1549 7 Dar es Salaam 1 1 Kinondoni 183 Bunju 1 Bunju A 71183001 1 4776 7 Dar es Salaam 1 1 Kinondoni 183 Bunju 2 Bunju B 71183002 1 3355 7 Dar es Salaam 1 1 Kinondoni 183 Bunju 3 Mabwepande 71183003 1 1927 7 Dar es Salaam 1 1 Kinondoni 183 Bunju 4 Mbopo 71183004 1 661 7 Dar es Salaam 1 1 Kinondoni 263 Mbezi 1 Makabe 71263001 1 6536 7 Dar es Salaam 1 1 Kinondoni 263 Mbezi 2 Mbezi Inn 71263002 1 5425 7 Dar es Salaam 1 1 Kinondoni 263 Mbezi 3 Msakuzi 71263003 1 2029 7 Dar es Salaam 1 1 Kinondoni 263 Mbezi 4 Mpiji Magoe 71263004 1 1799 7 Dar es Salaam 1 1 Kinondoni 263 Mbezi 5 Msumi 71263005 1 1751 7 Dar es salaam 2 2 Ilala 23 Pugu 1 Bangulo - Pugu station 72023001 1 1137 7 Dar es salaam 2 2 Ilala 31 Msongola 1 Msongola - Mbondole 72031001 1 3434 7 Dar es salaam 2 2 Ilala 31 Msongola 2 Mvuti - Mkela 72031002 1 3881 7 Dar es salaam 2 2 Ilala 51 Kinyerezi 1 Kinyerezi 72051001 1 5811 7 Dar es salaam 2 2 Ilala 213 Kitunda 1 Mzinga 72213001 1 4118 77 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 7 Dar es salaam 2 2 Ilala 213 Kitunda 2 Kivule 72213002 1 4358 7 Dar es salaam 2 2 Ilala 213 Kitunda 3 Kipunguni 'B' 72213003 1 6039 7 Dar es salaam 2 2 Ilala 223 Chanika 1 Buyuni - Mgeule 72223001 1 4553 7 Dar es salaam 2 2 Ilala 223 Chanika 2 Yongwe - Chanika I 72223002 1 6957 7 Dar es salaam 2 2 Ilala 223 Chanika 3 Chanika II - Rubakaya 72223003 1 5344 7 Dar es Salaam 3 3 Temeke 21 Vijibweni 1 Kibene 73021001 1 1025 7 Dar es Salaam 3 3 Temeke 21 Vijibweni 2 Vijibweni 73021002 1 2371 7 Dar es Salaam 3 3 Temeke 21 Vijibweni 3 Kisiwani 73021003 1 1061 7 Dar es Salaam 3 3 Temeke 21 Vijibweni 4 Mkwajuni 73021004 1 740 7 Dar es Salaam 3 3 Temeke 31 Kibada 1 Mizimbini 73031001 1 1744 7 Dar es Salaam 3 3 Temeke 31 Kibada 2 Mkize 73031002 1 1561 7 Dar es Salaam 3 3 Temeke 41 Kisarawe II 1 Tumaini 73041001 1 2453 7 Dar es Salaam 3 3 Temeke 41 Kisarawe II 2 Chekeni - Mwasonga 73041002 1 1810 7 Dar es Salaam 3 3 Temeke 51 Somangira 1 Kizani -Gezaulole 73051001 1 3892 7 Dar es Salaam 3 3 Temeke 51 Somangira 2 Mwongozo 73051002 1 2366 7 Dar es Salaam 3 3 Temeke 51 Somangira 3 Amani gomvu 73051003 1 4541 7 Dar es Salaam 3 3 Temeke 61 Kimbiji 1 Kizito Huonjwa -A 73061001 1 3673 7 Dar es Salaam 3 3 Temeke 83 Chamazi 1 Mbande 73083001 1 3241 7 Dar es Salaam 3 3 Temeke 113 Toangoma 1 Yasemwayo 73113001 1 2224 7 Dar es Salaam 3 3 Temeke 113 Toangoma 2 Mwanamsekwa 73113002 1 1619 7 Dar es Salaam 3 3 Temeke 231 Pemba Mnazi 1 Yaleyale Puna 73231001 1 1531 7 Dar es Salaam 3 3 Temeke 231 Pemba Mnazi 2 Buyuni 73231002 1 1085 7 Dar es Salaam 3 3 Temeke 231 Pemba Mnazi 3 Pemba Mnazi 73231003 1 531 7 Dar es Salaam 3 3 Temeke 231 Pemba Mnazi 4 Tundwi Songani 73231004 1 1992 7 Dar es Salaam 3 3 Temeke 241 Mji mwema 1 Maweni 73241001 1 3228 7 Dar es Salaam 3 3 Temeke 241 Mji mwema 2 Mji mwema 73241002 1 2490 7 Dar es Salaam 3 3 Temeke 241 Mji mwema 3 Kibugumo 73241003 1 1889 7 Dar es Salaam 3 3 Temeke 241 Mji mwema 4 Ugindoni 73241004 1 1480 8 Lindi 1 1 Kilwa 11 Tingi 1 Njianne 81011001 1 2738 8 Lindi 1 1 Kilwa 11 Tingi 3 Mtandango 81011003 1 973 8 Lindi 1 1 Kilwa 21 Miteja 3 Mtoni 81021003 1 2212 8 Lindi 1 1 Kilwa 31 Mingumbi 3 Mingumbi 81031003 1 2344 8 Lindi 1 1 Kilwa 31 Mingumbi 6 Nampunga 81031006 1 1169 8 Lindi 1 1 Kilwa 41 Kinjumbi 3 Kinjumbi A 81041003 1 2867 8 Lindi 1 1 Kilwa 41 Kinjumbi 5 Pungutini 81041005 1 1004 8 Lindi 1 1 Kilwa 51 Chumo 3 Ingirito 81051003 1 2664 8 Lindi 1 1 Kilwa 51 Chumo 4 Namayuni 81051004 1 5770 8 Lindi 1 1 Kilwa 61 Kipatimu 2 Mtondo Kimwaga 81061002 1 3553 8 Lindi 1 1 Kilwa 61 Kipatimu 5 Nandete 81061005 1 3520 8 Lindi 1 1 Kilwa 61 Kipatimu 8 Hanga 81061008 1 2453 8 Lindi 1 1 Kilwa 61 Kipatimu 9 Kibata 81061009 1 1941 78 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 8 Lindi 1 1 Kilwa 71 Kandawale 1 Kandalawe B 81071001 1 2562 8 Lindi 1 1 Kilwa 81 Njinjo 3 Mchemela 81081003 1 1634 8 Lindi 1 1 Kilwa 101 Miguruwe 2 Zinga Kibaoni 81101002 1 1241 8 Lindi 1 1 Kilwa 121 Nanjirinji 1 Nanjilinji A 81121001 1 1920 8 Lindi 1 1 Kilwa 131 Kiranjeranje 2 Mbwemkuru 81131002 1 1361 8 Lindi 1 1 Kilwa 131 Kiranjeranje 4 Makangaga 81131004 1 2166 8 Lindi 1 1 Kilwa 141 Mandawa 2 Kiwawa 81141002 1 1490 8 Lindi 1 1 Kilwa 141 Mandawa 5 Mandawa 81141005 1 4445 8 Lindi 1 1 Kilwa 151 Lihimalyao 2 Lihimalyao Kask 81151002 1 1741 8 Lindi 1 1 Kilwa 151 Lihimalyao 5 Rushungi 81151005 1 1030 8 Lindi 1 1 Kilwa 161 Pande 3 Namwedo 81161003 1 1281 8 Lindi 1 1 Kilwa 161 Pande 7 Malalani 81161007 1 1248 8 Lindi 1 1 Kilwa 183 Kivinje/Singino 3 Singino 81183003 1 4584 8 Lindi 1 1 Kilwa 183 Kivinje/Singino 4 Matandu 81183004 1 2157 8 Lindi 2 2 Lindi Rura 11 Mipingo 3 Lihimilo 82011003 1 1040 8 Lindi 2 2 Lindi Rura 31 Mchinga 1 Mchinga-Ruvu 82031001 1 2700 8 Lindi 2 2 Lindi Rura 31 Mchinga 4 Kilangala 82031004 1 3856 8 Lindi 2 2 Lindi Rura 41 Kilolambwani 2 Mvuleni "A" 82041002 1 1478 8 Lindi 2 2 Lindi Rura 51 Mbanja 2 Kikwetu 82051002 1 1321 8 Lindi 2 2 Lindi Rura 73 Mingoyo 1 Mkwaya 82073001 1 1464 8 Lindi 2 2 Lindi Rura 81 Mnolela 2 Zingatia 82081002 1 2754 8 Lindi 2 2 Lindi Rura 81 Mnolela 5 Namunda-Nusura 82081005 1 1724 8 Lindi 2 2 Lindi Rura 91 Sudi 3 Madangwa 82091003 1 2281 8 Lindi 2 2 Lindi Rura 101 Nachunyu 4 Nachunyu 82101004 1 4722 8 Lindi 2 2 Lindi Rura 113 Mtama 6 Mbalala 82113006 1 508 8 Lindi 2 2 Lindi Rura 123 Nyangao 4 Namangale 82123004 1 4334 8 Lindi 2 2 Lindi Rura 131 Namupa 3 Mnamba 82131003 1 566 8 Lindi 2 2 Lindi Rura 151 Mtua 2 Kilimahewa "A" 82151002 1 1819 8 Lindi 2 2 Lindi Rura 151 Mtua 4 Nalwadi 82151004 1 632 8 Lindi 2 2 Lindi Rura 161 Nahukahuka 4 Longa 82161004 1 850 8 Lindi 2 2 Lindi Rura 171 Ngangamara 4 Linoha 82171004 1 1190 8 Lindi 2 2 Lindi Rura 181 Mandwanga 3 Chiuta 82181003 1 2262 8 Lindi 2 2 Lindi Rura 201 Chiponda 1 Chiponda 82201001 1 1700 8 Lindi 2 2 Lindi Rura 211 Ng`apa 1 Mkupama 82211001 1 1985 8 Lindi 2 2 Lindi Rura 211 Ng`apa 2 Mbuyuni 82211002 1 2845 8 Lindi 2 2 Lindi Rura 221 Tandangongoro 1 Mkanga I 82221001 1 720 8 Lindi 2 2 Lindi Rura 231 Rutamba 2 Rutamba ya sasa 82231002 1 3916 8 Lindi 2 2 Lindi Rura 231 Rutamba 4 Kinyope 82231004 1 2389 8 Lindi 2 2 Lindi Rura 241 Milola 2 Milola Mashariki 82241002 1 3080 8 Lindi 2 2 Lindi Rura 251 Kiwawa 2 Kiwawa 82251002 1 1892 8 Lindi 2 2 Lindi Rura 261 Chikonji 3 Chikonji 82261003 1 3419 79 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 8 Lindi 3 3 Nachingwea 31 Ruponda 1 Ruponda 83031001 1 2395 8 Lindi 3 3 Nachingwea 31 Ruponda 4 Mandawa 83031004 1 999 8 Lindi 3 3 Nachingwea 41 Mnero 3 Ntila 83041003 1 1401 8 Lindi 3 3 Nachingwea 51 Namapwia 2 Likongowele 83051002 1 1493 8 Lindi 3 3 Nachingwea 61 Kipara Mnero 2 Nambalapala 83061002 1 1877 8 Lindi 3 3 Nachingwea 71 Lionja 2 Lionja 'A' 83071002 1 3008 8 Lindi 3 3 Nachingwea 71 Lionja 5 Ngunichile 83071005 1 2373 8 Lindi 3 3 Nachingwea 91 Nditi 1 Nditi 83091001 1 2914 8 Lindi 3 3 Nachingwea 101 Kilima Rondo 2 Kilima Rondo 83101002 1 1608 8 Lindi 3 3 Nachingwea 111 Mbondo 2 Mbondo 83111002 1 2182 8 Lindi 3 3 Nachingwea 121 Kiegei 1 Kiegei 83121001 1 3151 8 Lindi 3 3 Nachingwea 131 Mkoka 4 Rweje 83131004 1 1624 8 Lindi 3 3 Nachingwea 141 Chiola 4 Mtimbo 83141004 1 1145 8 Lindi 3 3 Nachingwea 151 Mpiruka 2 Mpiruka 83151002 1 3286 8 Lindi 3 3 Nachingwea 161 Nangowe 3 Matankini 83161003 1 2562 8 Lindi 3 3 Nachingwea 171 Mkotokuyana 1 Mkotokuyana 83171001 1 1351 8 Lindi 3 3 Nachingwea 183 Naipanga 1 Raha Leo 83183001 1 3172 8 Lindi 3 3 Nachingwea 191 Stesheni 2 Songambele 83191002 1 1552 8 Lindi 3 3 Nachingwea 201 Naipingo 1 Naipingo 83201001 1 2996 8 Lindi 3 3 Nachingwea 201 Naipingo 3 Mchonda 83201003 1 1726 8 Lindi 3 3 Nachingwea 201 Naipingo 6 Nang'ondo 83201006 1 1613 8 Lindi 3 3 Nachingwea 211 Mtua 1 Kipara Mtua 83211001 1 1715 8 Lindi 3 3 Nachingwea 221 Mnero Ngongo 1 Kitandi 83221001 1 1311 8 Lindi 3 3 Nachingwea 231 Matekwe 2 Matekwe 83231002 1 2078 8 Lindi 3 3 Nachingwea 241 Marambo 2 Marambo 83241002 1 2742 8 Lindi 3 3 Nachingwea 241 Marambo 5 Ikungu 83241005 1 822 8 Lindi 3 3 Nachingwea 261 Ndomoni 2 Ndomoni 83261002 1 1378 8 Lindi 4 4 Liwale 13 Liwale mjini 1 Nangando 84013001 1 1784 8 Lindi 4 4 Liwale 13 Liwale mjini 3 Mungurumo 84013003 1 1566 8 Lindi 4 4 Liwale 21 Mihumo 1 Likombora 84021001 1 1239 8 Lindi 4 4 Liwale 21 Mihumo 2 Mihumo 84021002 1 2558 8 Lindi 4 4 Liwale 31 Ngongowele 1 Ngongowele 84031001 1 1967 8 Lindi 4 4 Liwale 31 Ngongowele 3 Likombe 84031003 1 2579 8 Lindi 4 4 Liwale 41 Mlembwe 1 Mlembwe 84041001 1 1871 8 Lindi 4 4 Liwale 41 Mlembwe 2 Ndapata 84041002 1 411 8 Lindi 4 4 Liwale 51 Makata 2 Mkundi 84051002 1 1438 8 Lindi 4 4 Liwale 51 Makata 3 Mpengele 84051003 1 2188 8 Lindi 4 4 Liwale 61 Barikiwa 1 Chimbuko 84061001 1 1470 8 Lindi 4 4 Liwale 61 Barikiwa 3 Barikiwa 84061003 1 2176 8 Lindi 4 4 Liwale 71 Mkutano 2 Kikulyungu 84071002 1 1036 8 Lindi 4 4 Liwale 81 Mbaya 1 Kichonda 84081001 1 3413 80 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 8 Lindi 4 4 Liwale 81 Mbaya 2 Mbaya 84081002 1 2254 8 Lindi 4 4 Liwale 81 Mbaya 5 Mtawango 84081005 1 860 8 Lindi 4 4 Liwale 101 Kiang'ara 1 Kiangara 84101001 1 1548 8 Lindi 4 4 Liwale 101 Kiang'ara 3 Mtawatawa 84101003 1 921 8 Lindi 4 4 Liwale 111 Ndumbu 1 Ngumbu 84111001 1 1269 8 Lindi 4 4 Liwale 121 Nangano 1 Nangano 84121001 1 605 8 Lindi 4 4 Liwale 121 Nangano 2 Nahoro 84121002 1 1374 8 Lindi 4 4 Liwale 141 Mirui 1 Mirui 84141001 1 2071 8 Lindi 4 4 Liwale 141 Mirui 2 Mirui 84141002 1 656 8 Lindi 4 4 Liwale 151 Liwale 'B' 1 Mikunya 84151001 1 1435 8 Lindi 4 4 Liwale 151 Liwale 'B' 2 Liwale 'B' 84151002 1 4898 8 Lindi 4 4 Liwale 161 Mangirikiti 1 Kipule 84161001 1 2278 8 Lindi 4 4 Liwale 161 Mangirikiti 2 Mangirikiti 84161002 1 2832 8 Lindi 5 5 Ruangwa 13 Ruangwa 3 Lipande 85013003 1 394 8 Lindi 5 5 Ruangwa 23 Mbekenyera 5 Namilema 85023005 1 1978 8 Lindi 5 5 Ruangwa 33 Nkowe 2 Mpumbe 85033002 1 1057 8 Lindi 5 5 Ruangwa 41 Malolo 2 Mtakuja 85041002 1 2012 8 Lindi 5 5 Ruangwa 41 Malolo 5 Nangumbu 85041005 1 2874 8 Lindi 5 5 Ruangwa 41 Malolo 6 Michenga 85041006 1 3946 8 Lindi 5 5 Ruangwa 51 Luchelegwa 2 Chinongwe 85051002 1 4323 8 Lindi 5 5 Ruangwa 51 Luchelegwa 3 Luchelegwa 85051003 1 1602 8 Lindi 5 5 Ruangwa 51 Luchelegwa 6 Likwachu 85051006 1 2259 8 Lindi 5 5 Ruangwa 61 Chienjere 2 Chienjere 85061002 1 3415 8 Lindi 5 5 Ruangwa 61 Chienjere 4 Mibure 85061004 1 2015 8 Lindi 5 5 Ruangwa 71 Namichiga 2 Namichiga 85071002 1 2624 8 Lindi 5 5 Ruangwa 71 Namichiga 4 Matambarale 85071004 1 3096 8 Lindi 5 5 Ruangwa 81 Narungombe 1 Liuguru 85081001 1 2124 8 Lindi 5 5 Ruangwa 81 Narungombe 4 Machang'anja 85081004 1 569 8 Lindi 5 5 Ruangwa 91 Makanjiro 5 Chinokole 85091005 1 748 8 Lindi 5 5 Ruangwa 101 Likunja 2 Likunja 85101002 1 1705 8 Lindi 5 5 Ruangwa 101 Likunja 4 Mpara 85101004 1 585 8 Lindi 5 5 Ruangwa 111 Mnacho 1 Ng'au 85111001 1 3224 8 Lindi 5 5 Ruangwa 111 Mnacho 5 Namahema 85111005 1 2261 8 Lindi 5 5 Ruangwa 111 Mnacho 6 Nandagala 85111006 1 4035 8 Lindi 5 5 Ruangwa 121 Mandawa 1 Mchichili 85121001 1 2986 8 Lindi 5 5 Ruangwa 121 Mandawa 5 Chibula 85121005 1 1177 8 Lindi 5 5 Ruangwa 131 Nambilanje 1 Nanjaru 85131001 1 889 8 Lindi 5 5 Ruangwa 131 Nambilanje 4 Nambilanje 85131004 1 1521 8 Lindi 5 5 Ruangwa 141 Chunyu 2 Chunyu 85141002 1 1772 8 Lindi 5 5 Ruangwa 151 Mandarawe 1 Nandenje 85151001 1 1523 8 Lindi 6 6 Lindi Urba 103 Rasbura 1 Mitwero 86103001 1 1677 81 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 8 Lindi 6 6 Lindi Urba 113 Mtanda 1 Kineng'ene 86113001 1 3990 8 Lindi 6 6 Lindi Urba 123 Jamhuri 1 Mtange 86123001 1 215 8 Lindi 6 6 Lindi Urba 123 Jamhuri 2 Hyato 86123002 1 406 8 Lindi 6 6 Lindi Urba 123 Jamhuri 3 Tulieni Kiduni 86123003 1 2903 8 Lindi 6 6 Lindi Urba 133 Msinjahili 1 Nachingwea 86133001 1 2760 9 Mtwara 1 1 Mtwara R 13 Madimba 3 Mitambo 91013003 1 1464 9 Mtwara 1 1 Mtwara R 21 Ziwani 1 Msanga Mkuu 91021001 1 3486 9 Mtwara 1 1 Mtwara R 21 Ziwani 8 Minyembe 91021008 1 1139 9 Mtwara 1 1 Mtwara R 31 Nanguruwe 4 Mbawala 91031004 1 2895 9 Mtwara 1 1 Mtwara R 41 Mahurunga 1 Kihimika 91041001 1 1142 9 Mtwara 1 1 Mtwara R 41 Mahurunga 3 Kilambo 91041003 1 2326 9 Mtwara 1 1 Mtwara R 41 Mahurunga 7 Kirombelo 91041007 1 1394 9 Mtwara 1 1 Mtwara R 51 Kitaya 5 Kitaya 91051005 1 3149 9 Mtwara 1 1 Mtwara R 61 Kiromba 2 Kiromba/Ligula 91061002 1 2750 9 Mtwara 1 1 Mtwara R 61 Kiromba 4 Mpanyani 91061004 1 1333 9 Mtwara 1 1 Mtwara R 71 Njengwa 4 Nang'awanga 91071004 1 903 9 Mtwara 1 1 Mtwara R 93 Nanyamba 1 Mibobo 91093001 1 779 9 Mtwara 1 1 Mtwara R 93 Nanyamba 4 Mbembaleo 91093004 1 3762 9 Mtwara 1 1 Mtwara R 101 Mtiniko 3 Mtimbwilimbwi 91101003 1 1934 9 Mtwara 1 1 Mtwara R 101 Mtiniko 9 Maranje 91101009 1 2273 9 Mtwara 1 1 Mtwara R 111 Dihimba 4 Dihimba 91111004 1 1452 9 Mtwara 1 1 Mtwara R 121 Mnima 1 Lipwidi 91121001 1 1615 9 Mtwara 1 1 Mtwara R 121 Mnima 6 Mnima 91121006 1 3611 9 Mtwara 1 1 Mtwara R 131 Kitere 3 Nakada 91131003 1 1221 9 Mtwara 1 1 Mtwara R 131 Kitere 7 Libobe 91131007 1 3003 9 Mtwara 1 1 Mtwara R 141 Ndumbwe 4 Mbuo 91141004 1 1843 9 Mtwara 1 1 Mtwara R 151 Mayanga 3 Mkunwa 91151003 1 1599 9 Mtwara 1 1 Mtwara R 161 Naumbu 2 Mgao 91161002 1 1516 9 Mtwara 1 1 Mtwara R 161 Naumbu 4 Naumbu 91161004 1 2375 9 Mtwara 1 1 Mtwara R 181 Namtumbuka 1 Mnyai 91181001 1 1193 9 Mtwara 1 1 Mtwara R 181 Namtumbuka 3 Mnyawi 91181003 1 2731 9 Mtwara 1 1 Mtwara R 181 Namtumbuka 5 Klikwaya 91181005 1 1999 9 Mtwara 2 2 Newala 21 Makote 1 Mahumbika Mtua 92021001 1 1423 9 Mtwara 2 2 Newala 21 Makote 4 Makondeko-Chikongola 92021004 1 2010 9 Mtwara 2 2 Newala 31 Nanguruwe 5 Samora 92031005 1 969 9 Mtwara 2 2 Newala 41 Mkunya 1 Matokeo Mtandi &Mnauke 92041001 1 627 9 Mtwara 2 2 Newala 51 Mcholi I 1 Amani 92051001 1 1651 9 Mtwara 2 2 Newala 61 Namiyonga 1 Magombo 92061001 1 1825 9 Mtwara 2 2 Newala 61 Namiyonga 4 Namiyonga 92061004 1 1368 9 Mtwara 2 2 Newala 71 Mnekachi 2 NanyondaKazamoyo 92071002 1 1260 82 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 9 Mtwara 2 2 Newala 71 Mnekachi 3 Juhudi “C” 92071003 1 1684 9 Mtwara 2 2 Newala 91 Mnyambe 3 Mnima 92091003 1 1328 9 Mtwara 2 2 Newala 91 Mnyambe 4 Mnyambe 92091004 1 1915 9 Mtwara 2 2 Newala 101 Chilangala 1 Mkudumba 92101001 1 455 9 Mtwara 2 2 Newala 101 Chilangala 4 Chilangala 92101004 1 1434 9 Mtwara 2 2 Newala 111 Mkoma II 5 Mkoma II- Lihanga 92111005 1 1059 9 Mtwara 2 2 Newala 123 Kitangari 3 Niamoja Nachilindima 92123003 1 1251 9 Mtwara 2 2 Newala 131 Malatu 1 Malatu Juu- Sokoni/Kitang. 92131001 1 2327 9 Mtwara 2 2 Newala 141 Mchemo 3 Mdimba Mpelepele- Chitama 92141003 1 1344 9 Mtwara 2 2 Newala 141 Mchemo 4 Songambele Nameno 92141004 1 1159 9 Mtwara 2 2 Newala 151 Mtopwa 2 Chilondolo Mkungulu 92151002 1 1567 9 Mtwara 2 2 Newala 161 Chiwonga 2 Muungano Misufini 92161002 1 1413 9 Mtwara 2 2 Newala 161 Chiwonga 3 Mmulunga Bondeni 92161003 1 1900 9 Mtwara 2 2 Newala 171 Maputi 1 Mtongwele Chikongola 92171001 1 1737 9 Mtwara 2 2 Newala 181 Makukwe 1 Ngongo Kilimani 92181001 1 839 9 Mtwara 2 2 Newala 181 Makukwe 5 Makukwe Mkwajuni 92181005 1 1966 9 Mtwara 2 2 Newala 181 Makukwe 7 Mtunguru Namatu 92181007 1 3274 9 Mtwara 2 2 Newala 191 Mkwedu 2 Tengulengu Makule 92191002 1 2083 9 Mtwara 2 2 Newala 201 Mcholi II 2 Mnaida Kilimahewa 92201002 1 549 9 Mtwara 3 3 Masasi 21 Lisekese 3 Nangose/Kaunda 93021003 1 1971 9 Mtwara 3 3 Masasi 21 Lisekese 7 Matawale/Amani/ Umoja 93021007 1 1213 9 Mtwara 3 3 Masasi 21 Lisekese 8 Tukaewote/Ngalinje 93021008 0 1182 9 Mtwara 3 3 Masasi 21 Lisekese 12 Sululu/Sululu A 93021012 1 2285 9 Mtwara 3 3 Masasi 31 Marika 2 Namatunu 93031002 1 2339 9 Mtwara 3 3 Masasi 41 Mpindimbi 1 Chanikanguo/ Kilimanjaro 93041001 1 2565 9 Mtwara 3 3 Masasi 41 Mpindimbi 6 Kanyimbi 93041006 1 1964 9 Mtwara 3 3 Masasi 53 Lukuledi 5 Mraushi/Mwanga wa hewa 93053005 1 2713 9 Mtwara 3 3 Masasi 53 Lukuledi 9 Mpanyani/Mapokezi/ Juhudi 93053009 1 1205 9 Mtwara 3 3 Masasi 61 Namatutwe 3 Mkwapa Mwinyi 93061003 0 1037 9 Mtwara 3 3 Masasi 61 Namatutwe 4 Namatutwe Chipinda 93061004 1 2828 9 Mtwara 3 3 Masasi 101 Chiwata 2 Chidya Namaunya 93101002 1 2236 9 Mtwara 3 3 Masasi 111 Chigugu 2 Chigugu Mpilipili 93111002 1 3123 9 Mtwara 3 3 Masasi 111 Chigugu 6 Chikukwe Tandika 93111006 1 3390 9 Mtwara 3 3 Masasi 123 Mwena 2 Mtunungu Kitunda 93123002 1 3815 9 Mtwara 3 3 Masasi 123 Mwena 5 Liputu Tuungane 93123005 1 3163 9 Mtwara 3 3 Masasi 133 Nanganga 3 Mumburu Mwena 93133003 1 2836 83 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 9 Mtwara 3 3 Masasi 223 Chiungutwa 1 Mpeta Rahaleo 93223001 1 2051 9 Mtwara 3 3 Masasi 231 Nanjota 1 Milunda Misufini 93231001 0 937 9 Mtwara 3 3 Masasi 231 Nanjota 2 Nanjota Mnazimmoja 93231002 1 2415 9 Mtwara 3 3 Masasi 241 Mbuyuni 3 Mbuyuni Ndenganamadi 93241003 1 2497 9 Mtwara 3 3 Masasi 263 Namalenga 6 Mvita Magogosita 93263006 1 825 9 Mtwara 3 3 Masasi 281 Mkululu 4 Mkululu Shuleni 93281004 1 2037 9 Mtwara 3 3 Masasi 281 Mkululu 6 Mfuto Lusonje 93281006 0 1460 9 Mtwara 3 3 Masasi 301 Mchauru 1 Mwitika 93301001 1 1253 9 Mtwara 3 3 Masasi 311 Mnavira 1 Mnavira 93311001 1 2290 9 Mtwara 3 3 Masasi 311 Mnavira 7 Manyuli/Mduhe 93311007 1 1326 9 Mtwara 4 4 Tandahimba 13 Tandahimba 2 Malamba 94013002 1 2395 9 Mtwara 4 4 Tandahimba 23 Kitama 3 Ng'ongolo 94023003 1 2640 9 Mtwara 4 4 Tandahimba 31 Michenjele 2 Michenjele 94031002 1 2602 9 Mtwara 4 4 Tandahimba 41 Mihambwe 2 Kisangani 94041002 1 1848 9 Mtwara 4 4 Tandahimba 41 Mihambwe 4 Mihambwe 94041004 1 2981 9 Mtwara 4 4 Tandahimba 51 Mkoreha 2 Namunda 94051002 1 2157 9 Mtwara 4 4 Tandahimba 61 Maundo 1 Namahonga 94061001 1 3019 9 Mtwara 4 4 Tandahimba 61 Maundo 3 Kunandundu 94061003 1 1672 9 Mtwara 4 4 Tandahimba 71 Naputa 3 Mwangaza 94071003 1 2652 9 Mtwara 4 4 Tandahimba 91 Mnyawa 1 Jangwani 94091001 1 2610 9 Mtwara 4 4 Tandahimba 91 Mnyawa 4 Mnyawa 94091004 1 1688 9 Mtwara 4 4 Tandahimba 91 Mnyawa 7 Pachani 94091007 1 762 9 Mtwara 4 4 Tandahimba 101 Mkundi 4 Chitoholi 94101004 1 1382 9 Mtwara 4 4 Tandahimba 111 Lukokoda 2 Ghana juu/chini 94111002 1 1182 9 Mtwara 4 4 Tandahimba 123 Mahuta 3 Nakayaka 94123003 1 1010 9 Mtwara 4 4 Tandahimba 133 Nanhyanga 3 Dinduma Shuleni 94133003 1 789 9 Mtwara 4 4 Tandahimba 141 Chingungwe 1 Kuchele 94141001 1 1924 9 Mtwara 4 4 Tandahimba 141 Chingungwe 4 Mkupete 94141004 1 1391 9 Mtwara 4 4 Tandahimba 151 Mdimba Mnyoma 6 Tukuru "B" 94151006 1 2801 9 Mtwara 4 4 Tandahimba 161 Milongodi 1 Namkomolela 94161001 1 1158 9 Mtwara 4 4 Tandahimba 171 Lyenje 2 Mwembe Mmoja 94171002 1 1501 9 Mtwara 4 4 Tandahimba 171 Lyenje 5 Mivanga 94171005 1 1824 9 Mtwara 4 4 Tandahimba 181 Chaume 5 Chaume 94181005 1 3155 9 Mtwara 4 4 Tandahimba 191 Mkonojowano 3 Chimbuko 94191003 1 1458 9 Mtwara 4 4 Tandahimba 201 Luagala 4 Litehu 94201004 1 1761 9 Mtwara 4 4 Tandahimba 201 Luagala 6 Mkola chini 94201006 1 1786 9 Mtwara 4 4 Tandahimba 211 Ngunja 5 Ngunja 94211005 1 2050 9 Mtwara 5 5 Mtwara U 33 Likombe 1 Chikoko 95033001 1 1580 9 Mtwara 5 5 Mtwara U 33 Likombe 2 Namayanga 95033002 1 2033 9 Mtwara 5 5 Mtwara U 83 Jangwani 1 Mabatini 95083001 1 2155 9 Mtwara 5 5 Mtwara U 123 Ufukoni 1 Mihambwe 95123001 1 3598 84 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 9 Mtwara 5 5 Mtwara U 123 Ufukoni 2 Mbawala Chini 95123002 1 2187 9 Mtwara 5 5 Mtwara U 123 Ufukoni 3 Mbae Mashariki 95123003 1 1394 9 Mtwara 3 6 Nanyumbu 71 Mikangaula 3 Kamundi Mnazimmoja 96071003 0 2087 9 Mtwara 3 6 Nanyumbu 71 Mikangaula 4 Mkwajuni Nawaje 96071004 1 2625 9 Mtwara 3 6 Nanyumbu 71 Mikangaula 7 Namatumbusi 96071007 1 2302 9 Mtwara 3 6 Nanyumbu 81 Maratani 3 Holola Mitimingi 96081003 0 1860 9 Mtwara 3 6 Nanyumbu 81 Maratani 4 Mnanje chini Umoja 96081004 1 2060 9 Mtwara 3 6 Nanyumbu 81 Maratani 8 Lipupu K/hewa 96081008 0 993 9 Mtwara 3 6 Nanyumbu 91 Nandete 3 Nandete SIDO 96091003 1 2277 9 Mtwara 3 6 Nanyumbu 91 Nandete 5 Nakole Kisiwani 96091005 0 1623 9 Mtwara 3 6 Nanyumbu 141 Napacho 1 Nakopi Mwenge 96141001 1 1985 9 Mtwara 3 6 Nanyumbu 141 Napacho 3 Mburusa Misufini 96141003 0 1340 9 Mtwara 3 6 Nanyumbu 151 Lumesule 2 Chigweje, Changwale, migombani 96151002 1 2134 9 Mtwara 3 6 Nanyumbu 151 Lumesule 3 Nandembo Magomeni 96151003 0 2725 9 Mtwara 3 6 Nanyumbu 163 Likokona 2 Msinyasi/Msinyasi juu 96163002 1 2458 9 Mtwara 3 6 Nanyumbu 171 Mkonona 2 Namijati Zahanati 96171002 0 1192 9 Mtwara 3 6 Nanyumbu 181 Masuguru 1 Lukwika 96181001 0 209 9 Mtwara 3 6 Nanyumbu 181 Masuguru 3 Lukula Elimu 96181003 1 1742 9 Mtwara 3 6 Nanyumbu 191 Nanyumbu 4 Mkuula Mnawa 96191004 0 1275 9 Mtwara 3 6 Nanyumbu 191 Nanyumbu 6 Nanderu Umoja 96191006 1 1499 9 Mtwara 3 6 Nanyumbu 203 Nangomba 2 Mwambani 96203002 0 261 9 Mtwara 3 6 Nanyumbu 203 Nangomba 4 Nangomba Rahaleo 96203004 1 4216 9 Mtwara 3 6 Nanyumbu 203 Nangomba 7 Nahawara Mzalendo 96203007 0 757 9 Mtwara 3 6 Nanyumbu 203 Nangomba 9 Ngalinje Kagera 96203009 1 348 9 Mtwara 3 6 Nanyumbu 321 Namajani 2 Mlingula 96321002 1 2518 9 Mtwara 3 6 Nanyumbu 331 Chipuputa 1 Mkohora Kilimahihewa 96331001 0 1998 9 Mtwara 3 6 Nanyumbu 331 Chipuputa 5 Namaguluvi Songambele 96331005 0 2854 9 Mtwara 3 6 Nanyumbu 341 Sengenya 4 Mara 96341004 0 544 9 Mtwara 3 6 Nanyumbu 341 Sengenya 5 Sengenya/Amani 96341005 1 2001 10 Ruvuma 1 1 Tunduru 11 Kalulu 3 Kajima 101011003 1 1223 10 Ruvuma 1 1 Tunduru 21 Ligunga 3 Ligunga - Utukuru 101021003 1 3991 10 Ruvuma 1 1 Tunduru 33 M/Mashariki 4 Sisi kwa sisi 101033004 1 2233 10 Ruvuma 1 1 Tunduru 41 Mindu 3 Mtonya - Chikunja 101041003 1 2195 10 Ruvuma 1 1 Tunduru 51 Ngapa 1 Ngapa 101051001 1 1689 10 Ruvuma 1 1 Tunduru 61 Nakapanya 1 Nakapanya 101061001 1 4352 10 Ruvuma 1 1 Tunduru 71 Muhuwesi 1 Majimaji 101071001 1 6032 10 Ruvuma 1 1 Tunduru 71 Muhuwesi 2 Muhuwesi 101071002 1 2695 10 Ruvuma 1 1 Tunduru 71 Muhuwesi 3 Msagula 101071003 1 3562 10 Ruvuma 1 1 Tunduru 91 Ligoma 5 Ligoma 101091005 1 1884 10 Ruvuma 1 1 Tunduru 101 Misechela 4 Chiungo 101101004 1 2051 85 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 10 Ruvuma 1 1 Tunduru 111 Namasakata 4 Amani 101111004 1 4073 10 Ruvuma 1 1 Tunduru 121 Mtina 2 Semeni 101121002 1 3601 10 Ruvuma 1 1 Tunduru 131 Mchesi 1 Mwenge 101131001 1 1298 10 Ruvuma 1 1 Tunduru 141 Lukumbule 1 Imani 101141001 1 472 10 Ruvuma 1 1 Tunduru 151 Nalasi 2 Lipepo 101151002 1 2324 10 Ruvuma 1 1 Tunduru 151 Nalasi 5 Wenje 101151005 1 2560 10 Ruvuma 1 1 Tunduru 161 Mchoteka 3 Mchoteka 101161003 1 2094 10 Ruvuma 1 1 Tunduru 171 Marumba 3 Mbati 101171003 1 2483 10 Ruvuma 1 1 Tunduru 171 Marumba 6 Marumba 101171006 1 2887 10 Ruvuma 1 1 Tunduru 181 Mbesa 4 Lijombo 101181004 1 817 10 Ruvuma 1 1 Tunduru 193 Mlingoti Magharibi 1 Kitanda 101193001 1 2033 10 Ruvuma 1 1 Tunduru 193 Mlingoti Magharibi 4 National 101193004 1 5236 10 Ruvuma 1 1 Tunduru 201 Kidodoma 4 Kidodoma 101201004 1 2032 10 Ruvuma 1 1 Tunduru 211 Nandembo 5 Amka 101211005 1 1844 10 Ruvuma 1 1 Tunduru 221 Nampungu 3 Mbatamila 101221003 1 1730 10 Ruvuma 1 1 Tunduru 231 Matemanga 5 Milonde 101231005 1 1924 10 Ruvuma 2 2 Songea R 11 Wino 3 Lilondo 102011003 1 3379 10 Ruvuma 2 2 Songea R 11 Wino 4 Matetereka 102011004 1 2112 10 Ruvuma 2 2 Songea R 21 Ndongosi 2 Ndongosi 102021002 1 1909 10 Ruvuma 2 2 Songea R 41 Tanga 1 Kituro 102041001 1 799 10 Ruvuma 2 2 Songea R 41 Tanga 4 Tanga 102041004 1 3997 10 Ruvuma 2 2 Songea R 41 Tanga 6 Mlete 102041006 1 2428 10 Ruvuma 2 2 Songea R 51 Gumbiro 3 Gumbiro 102051003 1 1471 10 Ruvuma 2 2 Songea R 51 Gumbiro 5 Luhimba 102051005 1 2654 10 Ruvuma 2 2 Songea R 61 Mpitimbi 1 Mpitimbi 'A' 102061001 1 3095 10 Ruvuma 2 2 Songea R 61 Mpitimbi 3 Lyangweni 102061003 1 1391 10 Ruvuma 2 2 Songea R 61 Mpitimbi 5 Lipaya 102061005 1 2842 10 Ruvuma 2 2 Songea R 71 Muhukuru 2 Muhukuru - Barabarani 102071002 1 4566 10 Ruvuma 2 2 Songea R 71 Muhukuru 4 Magwamila 102071004 1 794 10 Ruvuma 2 2 Songea R 81 Magagura 3 Mbinga Mhalule 102081003 1 2030 10 Ruvuma 2 2 Songea R 81 Magagura 8 Lusonga 102081008 1 2235 10 Ruvuma 2 2 Songea R 91 Litisha 1 Liganga 102091001 1 2538 10 Ruvuma 2 2 Songea R 91 Litisha 3 Nakahuga 102091003 1 2257 10 Ruvuma 2 2 Songea R 91 Litisha 5 Magina 102091005 1 1631 10 Ruvuma 2 2 Songea R 101 Kilagano 3 Mgazini 102101003 1 3139 10 Ruvuma 2 2 Songea R 101 Kilagano 5 Lugagara 102101005 1 1441 10 Ruvuma 2 2 Songea R 113 Maposeni 4 Mdunduwalo 102113004 1 1700 10 Ruvuma 2 2 Songea R 121 Lilambo 1 Sinai 102121001 1 2472 10 Ruvuma 2 2 Songea R 121 Lilambo 2 Likuyufusi 102121002 1 2488 10 Ruvuma 2 2 Songea R 121 Lilambo 4 Mwanamonga 102121004 1 2013 10 Ruvuma 2 2 Songea R 133 Mahanje 2 Madaba 102133002 1 5021 86 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 10 Ruvuma 2 2 Songea R 133 Mahanje 3 Mkongotema 102133003 1 2352 10 Ruvuma 2 2 Songea R 141 Matimira 2 Mpangula 102141002 1 685 10 Ruvuma 3 3 Mbinga 11 Ruanda 3 Paradiso 103011003 1 1022 10 Ruvuma 3 3 Mbinga 33 Kigonsera 6 Amani Makoro 103033006 1 1708 10 Ruvuma 3 3 Mbinga 41 Kihangi Mahuka 4 Lipumba 103041004 1 2831 10 Ruvuma 3 3 Mbinga 63 Mbinga Urban 2 Matarawe 103063002 1 3447 10 Ruvuma 3 3 Mbinga 71 Kilimani 3 Kilimani 103071003 1 2614 10 Ruvuma 3 3 Mbinga 81 Mbangamao 8 Njoomlole 103081008 1 911 10 Ruvuma 3 3 Mbinga 91 Liparamba 4 Mitomoni 103091004 1 1458 10 Ruvuma 3 3 Mbinga 111 Chiwanda 4 Mtupale 103111004 1 1453 10 Ruvuma 3 3 Mbinga 133 Mbamba bay 1 Mbamba bay 103133001 1 798 10 Ruvuma 3 3 Mbinga 141 Kingerikiti 6 Lumecha 103141006 1 1333 10 Ruvuma 3 3 Mbinga 151 Nyoni 2 Likwela 103151002 1 1559 10 Ruvuma 3 3 Mbinga 173 Maguu 4 Maguu 103173004 1 3044 10 Ruvuma 3 3 Mbinga 173 Maguu 7 Mapera 103173007 1 4003 10 Ruvuma 3 3 Mbinga 191 Kihagara 3 Mango 103191003 1 2157 10 Ruvuma 3 3 Mbinga 201 Mikalanga 3 Ugano 103201003 1 2311 10 Ruvuma 3 3 Mbinga 211 Langiro 6 Langiro Asili 103211006 1 1572 10 Ruvuma 3 3 Mbinga 221 Mbuji 2 Kilanga Juu 103221002 1 2314 10 Ruvuma 3 3 Mbinga 241 Ngima 2 Unango 103241002 1 2627 10 Ruvuma 3 3 Mbinga 251 Myangayanga 3 Myangayanga 103251003 1 1593 10 Ruvuma 3 3 Mbinga 261 Mkumbi 4 Longa 103261004 1 3568 10 Ruvuma 3 3 Mbinga 271 Linda 5 Ndembo 103271005 1 2256 10 Ruvuma 3 3 Mbinga 283 Matiri 3 Kiyaha 103283003 1 2148 10 Ruvuma 3 3 Mbinga 301 Ngumbo 1 Ndonga 103301001 1 928 10 Ruvuma 3 3 Mbinga 311 Mbaha 2 Mbaha 103311002 1 2010 10 Ruvuma 3 3 Mbinga 331 Mpepai 5 Lipembe 103331005 1 1584 10 Ruvuma 3 3 Mbinga 341 Kilosa 4 Ruhekei 103341004 1 941 10 Ruvuma 3 3 Mbinga 371 Lituhi 1 Lituhi 103371001 1 4099 10 Ruvuma 4 4 Songea Urb 53 Lizaboni 1 London "A & B" 104053001 1 912 10 Ruvuma 4 4 Songea Urb 83 Matogoro 1 Mahilo 104083001 1 783 10 Ruvuma 4 4 Songea Urb 83 Matogoro 2 Ndirima litembo 104083002 1 1456 10 Ruvuma 4 4 Songea Urb 93 Ruvuma 1 Kipera juu 104093001 1 1287 10 Ruvuma 4 4 Songea Urb 101 Subira 1 Rupapila 104101001 1 2066 10 Ruvuma 4 4 Songea Urb 101 Subira 2 Subira 104101002 1 2131 10 Ruvuma 4 4 Songea Urb 101 Subira 3 Muungano,kisiwani b & mtengashari 104101003 1 2420 10 Ruvuma 4 4 Songea Urb 111 Ruhuwiko 1 Ruhuwiko 104111001 1 2583 10 Ruvuma 4 4 Songea Urb 111 Ruhuwiko 2 Namanditi "B" 104111002 1 893 10 Ruvuma 4 4 Songea Urb 111 Ruhuwiko 3 Mwengemshindo 104111003 1 1079 10 Ruvuma 4 4 Songea Urb 111 Ruhuwiko 4 Kiyogowale 104111004 1 1199 87 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 10 Ruvuma 4 4 Songea Urb 123 Mshangano 1 Chandalua 104123001 1 2117 10 Ruvuma 4 4 Songea Urb 123 Mshangano 2 Mshangano 104123002 1 2160 10 Ruvuma 4 4 Songea Urb 123 Mshangano 3 Msamala 104123003 1 1067 10 Ruvuma 4 4 Songea Urb 131 Mletele 1 Mletele 104131001 1 3394 10 Ruvuma 4 4 Songea Urb 131 Mletele 2 Luhira seko 104131002 1 2893 10 Ruvuma 4 4 Songea Urb 131 Mletele 3 Unangwa 104131003 1 1714 10 Ruvuma 5 5 Namtumbo 13 Rwinga 1 Minazini 105013001 1 972 10 Ruvuma 5 5 Namtumbo 21 Mkongo 3 Mwangaza 105021003 1 1859 10 Ruvuma 5 5 Namtumbo 21 Mkongo 5 Njalamata 105021005 1 2729 10 Ruvuma 5 5 Namtumbo 31 Ligera 3 Ligera 105031003 1 2610 10 Ruvuma 5 5 Namtumbo 31 Ligera 6 Mtelawamwahi 105031006 1 1284 10 Ruvuma 5 5 Namtumbo 41 Lusewa 2 Lusewa 105041002 1 4949 10 Ruvuma 5 5 Namtumbo 41 Lusewa 4 Matepwende 105041004 1 1557 10 Ruvuma 5 5 Namtumbo 51 Magazini 2 Likusanguse 105051002 1 1970 10 Ruvuma 5 5 Namtumbo 61 Msindo 1 Mageuzi 105061001 1 2741 10 Ruvuma 5 5 Namtumbo 61 Msindo 4 Hanga 105061004 1 5231 10 Ruvuma 5 5 Namtumbo 61 Msindo 5 Msindo 105061005 1 2220 10 Ruvuma 5 5 Namtumbo 71 Luchili 2 Mkongo Gulioni 105071002 1 4264 10 Ruvuma 5 5 Namtumbo 71 Luchili 4 Namanguli 105071004 1 3310 10 Ruvuma 5 5 Namtumbo 81 Namabengo 1 Mdwema 105081001 1 1174 10 Ruvuma 5 5 Namtumbo 81 Namabengo 3 Namabengo 105081003 1 5182 10 Ruvuma 5 5 Namtumbo 91 Kitanda 1 Kitanda 105091001 1 4885 10 Ruvuma 5 5 Namtumbo 91 Kitanda 3 Naikesi 105091003 1 6241 10 Ruvuma 5 5 Namtumbo 101 Luegu 1 Nahoro 105101001 1 2674 10 Ruvuma 5 5 Namtumbo 101 Luegu 2 Luegu 105101002 1 3448 10 Ruvuma 5 5 Namtumbo 101 Luegu 5 Litola 105101005 1 2823 10 Ruvuma 5 5 Namtumbo 101 Luegu 7 Kumbara 105101007 1 2095 10 Ruvuma 5 5 Namtumbo 113 Namtumbo 2 Suluti 105113002 1 5787 10 Ruvuma 5 5 Namtumbo 113 Namtumbo 5 Songambele 105113005 1 973 10 Ruvuma 5 5 Namtumbo 121 Mgombasi 1 Nambecha 105121001 1 3300 10 Ruvuma 5 5 Namtumbo 121 Mgombasi 3 Mtonya 105121003 1 3527 11 Iringa 1 1 Iringa R 11 Kalenga 1 Mkoga 111011001 1 959 11 Iringa 1 1 Iringa R 21 Kiwere 2 Kilondo 111021002 1 2224 11 Iringa 1 1 Iringa R 21 Kiwere 5 Mgongo 111021005 1 1659 11 Iringa 1 1 Iringa R 31 Nzihi 3 Nzihi 111031003 1 2960 11 Iringa 1 1 Iringa R 31 Nzihi 5 Ilalasimba 111031005 1 1350 11 Iringa 1 1 Iringa R 53 Mseke 1 Kaning'ombe 111053001 1 4064 11 Iringa 1 1 Iringa R 53 Mseke 4 Ugwachanya 111053004 1 3914 11 Iringa 1 1 Iringa R 61 Magulilwa 2 Ng'enza Kilolo 111061002 1 3969 11 Iringa 1 1 Iringa R 61 Magulilwa 5 Tagamenda Malulumo 111061005 1 3737 11 Iringa 1 1 Iringa R 61 Magulilwa 8 Ndiwili Msuluti 111061008 1 2655 88 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 11 Iringa 1 1 Iringa R 71 Mgama 3 Ilandutwa 111071003 1 2869 11 Iringa 1 1 Iringa R 71 Mgama 6 Ihemi 111071006 1 2119 11 Iringa 1 1 Iringa R 81 Ifunda 1 Ifunda 111081001 1 4961 11 Iringa 1 1 Iringa R 81 Ifunda 4 Kibena 111081004 1 2254 11 Iringa 1 1 Iringa R 101 Maboga 2 Igangidung'u - Isaka 'A & B', 111101002 1 2719 11 Iringa 1 1 Iringa R 101 Maboga 6 Makombe - Makongoni,Ukeleni & 111101006 1 1295 11 Iringa 1 1 Iringa R 111 Wasa 4 Ikungwe - Mkunzi,Ikungwe 111111004 1 1622 11 Iringa 1 1 Iringa R 121 Mahuninga 2 Mahuninga 111121002 1 2166 11 Iringa 1 1 Iringa R 131 Idodi 2 Mapogoro 111131002 1 2346 11 Iringa 1 1 Iringa R 141 Mlowa 2 Malinzanga - Matalawe 111141002 1 4295 11 Iringa 1 1 Iringa R 151 Itunundu 1 Mbuyuni 111151001 1 874 11 Iringa 1 1 Iringa R 151 Itunundu 5 Itunundu 111151005 1 2850 11 Iringa 1 1 Iringa R 161 Ilolompya 4 Luganga 111161004 1 1444 11 Iringa 1 1 Iringa R 171 Nduli 8 Kisunga 111171008 1 1085 11 Iringa 1 1 Iringa R 181 Kihorogota 3 Mangawe 111181003 1 2665 11 Iringa 1 1 Iringa R 181 Kihorogota 9 Ndolela 111181009 1 1388 11 Iringa 1 1 Iringa R 193 Izazi 3 Makatopora 111193003 1 1967 11 Iringa 2 2 Mufindi 11 Kiyowela 4 Idete 112011004 1 3106 11 Iringa 2 2 Mufindi 31 Mninga 3 Mninga 112031003 1 5880 11 Iringa 2 2 Mufindi 31 Mninga 4 Mkalala 112031004 1 1934 11 Iringa 2 2 Mufindi 41 Kasanga 2 Ihomasa 112041002 1 2306 11 Iringa 2 2 Mufindi 53 Igowole 2 Ibatu 112053002 1 1173 11 Iringa 2 2 Mufindi 61 Mtambula 3 Ipilimo 112061003 1 2687 11 Iringa 2 2 Mufindi 71 Itandula 3 Nyigo 112071003 1 1748 11 Iringa 2 2 Mufindi 81 Mbalamaziwa 2 Kitelewasi 112081002 1 1192 11 Iringa 2 2 Mufindi 91 Idunda 1 Idumilavanu 112091001 1 2213 11 Iringa 2 2 Mufindi 121 Ihowanza 1 Kwatwanga 112121001 1 1491 11 Iringa 2 2 Mufindi 131 Ikweha 1 Ikweha 112131001 1 2670 11 Iringa 2 2 Mufindi 141 Sadani 1 Ihatuzwa 112141001 1 954 11 Iringa 2 2 Mufindi 151 Igombavanu 1 Mapogoro 112151001 1 2128 11 Iringa 2 2 Mufindi 161 Bumilanga 2 Bumilayinga 112161002 1 1354 11 Iringa 2 2 Mufindi 173 Mafinga 3 Ndolezi 112173003 1 898 11 Iringa 2 2 Mufindi 173 Mafinga 5 Luganga 112173005 1 1825 11 Iringa 2 2 Mufindi 181 Isalavanu 3 Kikombo 112181003 1 2201 11 Iringa 2 2 Mufindi 191 Rungemba 3 Itimbo 112191003 1 2658 11 Iringa 2 2 Mufindi 201 Ifwagi 6 Itona 112201006 1 2442 11 Iringa 2 2 Mufindi 211 Mdabulo 3 Ikanga 112211003 1 1501 11 Iringa 2 2 Mufindi 221 Ihalimba 3 Wami 112221003 1 2141 89 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 11 Iringa 2 2 Mufindi 231 Kibengu 2 Usokami 112231002 1 4176 11 Iringa 2 2 Mufindi 231 Kibengu 6 Kipanga 112231006 1 2377 11 Iringa 2 2 Mufindi 241 Mapanda 4 Ukami 112241004 1 2822 11 Iringa 2 2 Mufindi 261 Ihanu 5 Ibwanzi 112261005 1 1763 11 Iringa 2 2 Mufindi 271 Luhunga 4 Mkonge 112271004 1 2866 11 Iringa 2 2 Mufindi 283 Mtwango 2 Sawala 112283002 1 4392 11 Iringa 3 3 Makete 11 Lupalilo 2 Mago 113011002 1 987 11 Iringa 3 3 Makete 11 Lupalilo 5 Tandala 113011005 1 1672 11 Iringa 3 3 Makete 11 Lupalilo 9 Kisinga 113011009 1 1017 11 Iringa 3 3 Makete 23 Iwawa 3 Ludihani 113023003 1 491 11 Iringa 3 3 Makete 23 Iwawa 5 Isapulano 113023005 1 1894 11 Iringa 3 3 Makete 31 Mang'oto 2 Mang'ota 113031002 1 852 11 Iringa 3 3 Makete 41 Lupila 1 Kijyombo 113041001 1 914 11 Iringa 3 3 Makete 41 Lupila 5 Ukange 113041005 1 1023 11 Iringa 3 3 Makete 51 Ukwama 3 Ukwama 113051003 1 1705 11 Iringa 3 3 Makete 61 Bulongwa 2 Ilolo 113061002 1 599 11 Iringa 3 3 Makete 61 Bulongwa 7 Uganga 113061007 1 816 11 Iringa 3 3 Makete 71 Kipagalo 1 Iyoka 113071001 1 1137 11 Iringa 3 3 Makete 71 Kipagalo 6 Kilanji 113071006 1 678 11 Iringa 3 3 Makete 81 Iniho 3 Iniho 113081003 1 876 11 Iringa 3 3 Makete 91 Ipelele 2 Ipelele 113091002 1 1409 11 Iringa 3 3 Makete 91 Ipelele 6 Makwaranga 113091006 1 1081 11 Iringa 3 3 Makete 113 Matamba 2 Kinyika 113113002 1 2232 11 Iringa 3 3 Makete 113 Matamba 4 Nhungu 113113004 1 1358 11 Iringa 3 3 Makete 121 Mlondwe 2 Mlondwe 113121002 1 1253 11 Iringa 3 3 Makete 121 Mlondwe 6 Magoye 113121006 1 1574 11 Iringa 3 3 Makete 131 Kitulo 1 Kikondo 113131001 1 2493 11 Iringa 3 3 Makete 131 Kitulo 4 Nkenja 113131004 1 1206 11 Iringa 3 3 Makete 141 Ikuwo 3 Kigala 113141003 1 1553 11 Iringa 3 3 Makete 151 Mfumbi 1 Mfumbi 113151001 1 2539 11 Iringa 3 3 Makete 151 Mfumbi 4 Usalimwani 113151004 1 513 11 Iringa 3 3 Makete 161 Ipepo 4 Maliwa 113161004 1 1219 11 Iringa 3 3 Makete 171 Mbalatse 2 Kisasatu 113171002 1 1001 11 Iringa 4 4 Njombe 21 Imalinyi 1 Igagala 114021001 0 4103 11 Iringa 4 4 Njombe 21 Imalinyi 4 Imalinyi 114021004 1 5125 11 Iringa 4 4 Njombe 21 Imalinyi 7 Kidugala 114021007 1 3453 11 Iringa 4 4 Njombe 31 Igosi 3 Utelewe 114031003 0 3507 11 Iringa 4 4 Njombe 31 Igosi 12 Makoga 114031012 0 2279 11 Iringa 4 4 Njombe 51 Wanging'ombe 2 Mng'elenge 114051002 1 1840 11 Iringa 4 4 Njombe 51 Wanging'ombe 6 Mayale 114051006 0 1179 11 Iringa 4 4 Njombe 61 Saja 1 Isimike 114061001 1 2315 90 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 11 Iringa 4 4 Njombe 61 Saja 3 Itengelo 114061003 0 1469 11 Iringa 4 4 Njombe 61 Saja 6 Saja 114061006 1 2540 11 Iringa 4 4 Njombe 73 Ilembula 6 Mpululu 114073006 0 463 11 Iringa 4 4 Njombe 73 Ilembula 14 Iponda 114073014 1 699 11 Iringa 4 4 Njombe 73 Ilembula 15 Igula 114073015 0 1110 11 Iringa 4 4 Njombe 101 Mahongole 2 Ibatu 114101002 0 1147 11 Iringa 4 4 Njombe 111 Igongolo 3 Tagamenda 114111003 1 1074 11 Iringa 4 4 Njombe 111 Igongolo 5 Igongolo 114111005 0 1970 11 Iringa 4 4 Njombe 121 Mtwango 3 Lunguya 114121003 1 4638 11 Iringa 4 4 Njombe 121 Mtwango 6 Mawande 114121006 0 1986 11 Iringa 4 4 Njombe 131 Ikuka 3 Ikuna 114131003 1 1506 11 Iringa 4 4 Njombe 131 Ikuka 6 Lima Igelehaza 114131006 0 946 11 Iringa 4 4 Njombe 141 Mdandu 7 Itambo/Mapila 114141007 0 1603 11 Iringa 4 4 Njombe 141 Mdandu 14 Lulanzi 114141014 0 1216 11 Iringa 4 4 Njombe 151 Usuka 5 Igwachanya 114151005 1 3786 11 Iringa 4 4 Njombe 151 Usuka 8 Kanani 114151008 0 1391 11 Iringa 4 4 Njombe 171 Kidegembye 2 Kidegembye 114171002 1 3962 11 Iringa 4 4 Njombe 181 Ikondo 1 Nyave 114181001 0 919 11 Iringa 4 4 Njombe 181 Ikondo 3 Ikondo 114181003 1 3446 11 Iringa 5 5 Ludewa 31 Mawengi 1 Kiwe 115031001 1 891 11 Iringa 5 5 Ludewa 31 Mawengi 3 Mawengi 115031003 1 3272 11 Iringa 5 5 Ludewa 31 Mawengi 5 Madunda 115031005 1 1954 11 Iringa 5 5 Ludewa 41 Lupanga 2 Utilili 115041002 1 1466 11 Iringa 5 5 Ludewa 53 Mlangali 1 Masimbwe 115053001 1 1855 11 Iringa 5 5 Ludewa 53 Mlangali 2 Kiyombo 115053002 1 3579 11 Iringa 5 5 Ludewa 53 Mlangali 4 Lufumbu 115053004 1 2109 11 Iringa 5 5 Ludewa 61 Milo 1 Mapogoro 115061001 1 3122 11 Iringa 5 5 Ludewa 61 Milo 2 Mavala 115061002 1 2377 11 Iringa 5 5 Ludewa 73 Lugarawa 1 Mdilidili 115073001 1 2161 11 Iringa 5 5 Ludewa 73 Lugarawa 3 Mkongobaki 115073003 1 1836 11 Iringa 5 5 Ludewa 81 Madope 1 Luvuyo 115081001 1 1921 11 Iringa 5 5 Ludewa 91 Madilu 1 Manga 115091001 1 2075 11 Iringa 5 5 Ludewa 91 Madilu 3 Madilu 115091003 1 3096 11 Iringa 5 5 Ludewa 91 Madilu 5 Ilininda 115091005 1 2103 11 Iringa 5 5 Ludewa 101 Mundindi 2 Mundindi 115101002 1 2361 11 Iringa 5 5 Ludewa 111 Mavanga 1 Mavanga 115111001 1 3478 11 Iringa 5 5 Ludewa 111 Mavanga 2 Mbugani 115111002 1 2425 11 Iringa 5 5 Ludewa 131 Nkomang'ombe 2 Nkomang'ombe 115131002 1 1987 11 Iringa 5 5 Ludewa 141 Luilo 3 Lifua 115141003 1 1751 11 Iringa 5 5 Ludewa 163 Manda 1 Mbongo 115163001 1 1424 11 Iringa 5 5 Ludewa 163 Manda 5 Kipingu 115163005 1 1040 91 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 11 Iringa 5 5 Ludewa 181 Lupingu 2 Mtumbati 115181002 1 1158 11 Iringa 5 5 Ludewa 193 Ludewa 1 Ludewa Kijijini 115193001 1 2153 11 Iringa 5 5 Ludewa 201 Ludende 2 Madindo 115201002 1 1636 11 Iringa 5 5 Ludewa 211 Luana 2 Luana 115211002 1 2421 11 Iringa 5 5 Ludewa 221 Makonde 1 Makonde 115221001 1 1817 11 Iringa 6 6 Iringa Urb 23 Mtwivila 1 Ugele 116023001 1 368 11 Iringa 6 6 Iringa Urb 53 Ruaha 1 Igumbiro 116053001 1 1243 11 Iringa 6 6 Iringa Urb 93 Mwangata 1 Mawelewele 116093001 1 555 11 Iringa 6 6 Iringa Urb 93 Mwangata 2 Kitasengwa 116093002 1 299 11 Iringa 6 6 Iringa Urb 93 Mwangata 3 Isakalilo 116093003 1 314 11 Iringa 6 6 Iringa Urb 133 Mkwawa 1 Itamba/Ilongo 116133001 1 1314 11 Iringa 7 7 Kilolo 11 Image 4 Lyasa 117011004 1 2776 11 Iringa 7 7 Kilolo 11 Image 6 Image 117011006 1 2874 11 Iringa 7 7 Kilolo 21 Irole 1 Lundamatwe 117021001 1 4499 11 Iringa 7 7 Kilolo 21 Irole 5 Mbigili 117021005 1 3567 11 Iringa 7 7 Kilolo 21 Irole 7 Imalutwa 117021007 1 4462 11 Iringa 7 7 Kilolo 21 Irole 9 Kitelewasi 117021009 1 2988 11 Iringa 7 7 Kilolo 33 Ilula 3 Mlafu 117033003 1 1661 11 Iringa 7 7 Kilolo 41 Uhambingeto 1 Uhambingeto 117041001 1 3158 11 Iringa 7 7 Kilolo 41 Uhambingeto 3 Kipaduka 117041003 1 2600 11 Iringa 7 7 Kilolo 61 Mahenge 1 Mahenge 117061001 1 1999 11 Iringa 7 7 Kilolo 61 Mahenge 4 Nyanzwa 117061004 1 2435 11 Iringa 7 7 Kilolo 61 Mahenge 9 Mtandika 117061009 1 2632 11 Iringa 7 7 Kilolo 71 Mtitu 2 Utengule 117071002 1 3090 11 Iringa 7 7 Kilolo 71 Mtitu 4 Itimbo 117071004 1 2782 11 Iringa 7 7 Kilolo 71 Mtitu 6 Kilolo 117071006 1 3912 11 Iringa 7 7 Kilolo 81 Dabaga 3 Magome - Ndengisivili,Ulefi 117081003 1 1802 11 Iringa 7 7 Kilolo 81 Dabaga 5 Ng'ang'ange - Mtakuja,Myakoles 117081005 1 4023 11 Iringa 7 7 Kilolo 91 Ukumbi 2 Mawambala 117091002 1 3896 11 Iringa 7 7 Kilolo 91 Ukumbi 4 Kitowo 117091004 1 2554 11 Iringa 7 7 Kilolo 91 Ukumbi 6 Ng'uruhe 117091006 1 3075 11 Iringa 7 7 Kilolo 101 Ukwega 2 Kisanga - Mabalala,Madisi,Kiba 117101002 1 2531 11 Iringa 7 7 Kilolo 101 Ukwega 6 Ukwega - Mnenuka,Nyahi,Uwanda 117101006 1 1819 11 Iringa 7 7 Kilolo 111 Boma la Ng'ombe 2 Boma la Ng'ombe 117111002 1 4730 11 Iringa 7 7 Kilolo 111 Boma la Ng'ombe 4 Idegenda 117111004 1 4138 11 Iringa 7 7 Kilolo 111 Boma la Ng'ombe 6 Mbawi 117111006 1 2317 11 Iringa 7 7 Kilolo 121 Idete 5 Itonya - Kanisani 117121005 1 1112 92 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 11 Iringa 7 7 Kilolo 121 Idete 9 Kiwalamo - Kusagi,Miegamano 117121009 1 1877 11 Iringa 4 8 Njombe Mji 201 Uwemba 1 Ihalula 118201001 1 3004 11 Iringa 4 8 Njombe Mji 201 Uwemba 2 Utalingolo 118201002 0 2485 11 Iringa 4 8 Njombe Mji 201 Uwemba 4 Uwemba 118201004 1 4547 11 Iringa 4 8 Njombe Mji 201 Uwemba 5 Njoomhole 118201005 0 1498 11 Iringa 4 8 Njombe Mji 201 Uwemba 6 Mkandaula 118201006 0 641 11 Iringa 4 8 Njombe Mji 211 Iwungilo 1 Igoma 118211001 0 1801 11 Iringa 4 8 Njombe Mji 211 Iwungilo 3 Ngalanga 118211003 0 1620 11 Iringa 4 8 Njombe Mji 211 Iwungilo 4 Uliwa 118211004 1 2691 11 Iringa 4 8 Njombe Mji 223 Luponde 1 Miva/Stoo 118223001 0 1732 11 Iringa 4 8 Njombe Mji 223 Luponde 3 Isitu/Mkengwa 118223003 0 1596 11 Iringa 4 8 Njombe Mji 223 Luponde 4 Lugenge/Madobole 118223004 0 1219 11 Iringa 4 8 Njombe Mji 231 Matola 1 Boimanda 118231001 0 1338 11 Iringa 4 8 Njombe Mji 231 Matola 2 Kitulila 118231002 1 2587 11 Iringa 4 8 Njombe Mji 231 Matola 4 Mbega 118231004 0 1509 11 Iringa 4 8 Njombe Mji 231 Matola 5 Makowo 118231005 0 2389 11 Iringa 4 8 Njombe Mji 231 Matola 7 Idihani 118231007 1 1372 11 Iringa 4 8 Njombe Mji 231 Matola 8 Mtila 118231008 0 2297 11 Iringa 4 8 Njombe Mji 241 Kifanya 1 Kifanya 118241001 0 3357 11 Iringa 4 8 Njombe Mji 241 Kifanya 2 Utengule 118241002 0 1017 11 Iringa 4 8 Njombe Mji 241 Kifanya 3 Lwangu 118241003 1 1959 11 Iringa 4 8 Njombe Mji 241 Kifanya 5 Ihanga 118241005 0 2569 11 Iringa 4 8 Njombe Mji 241 Kifanya 6 Itikula 118241006 0 1276 11 Iringa 4 8 Njombe Mji 241 Kifanya 8 Lilombwi 118241008 0 741 11 Iringa 4 8 Njombe Mji 241 Kifanya 9 Mikongo 118241009 0 1881 11 Iringa 4 8 Njombe Mji 251 Yakobi 1 Idunda 118251001 0 1368 11 Iringa 4 8 Njombe Mji 251 Yakobi 3 Limage 118251003 0 1721 11 Iringa 4 8 Njombe Mji 251 Yakobi 4 Nundu 118251004 1 1803 12 Mbeya 1 1 Chunya 11 Kambikatoto 1 Kambikatoto 121011001 1 1951 12 Mbeya 1 1 Chunya 31 Matwiga 2 Matwiga 121031002 1 1643 12 Mbeya 1 1 Chunya 41 Mtanila 3 Kalangali 121041003 1 1841 12 Mbeya 1 1 Chunya 51 L/ tingatinga 3 L/ Vitumbi 121051003 1 7064 12 Mbeya 1 1 Chunya 73 Makongorosi 1 Mkola 121073001 1 4796 12 Mbeya 1 1 Chunya 73 Makongorosi 2 Mkola 121073002 1 5820 12 Mbeya 1 1 Chunya 73 Makongorosi 4 Kitete 121073004 1 3649 12 Mbeya 1 1 Chunya 83 Itewe 4 Sinjilili 121083004 1 887 12 Mbeya 1 1 Chunya 93 Chokaa 2 Igodima 121093002 1 1982 12 Mbeya 1 1 Chunya 101 Mbugani 2 Mlimanjiwa 121101002 1 1533 12 Mbeya 1 1 Chunya 111 Chalangwa 1 Chalangwa 121111001 1 5522 12 Mbeya 1 1 Chunya 111 Chalangwa 2 Sangambi 121111002 1 2891 93 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 12 Mbeya 1 1 Chunya 121 Ifumbo 1 Ifumbo 121121001 1 5391 12 Mbeya 1 1 Chunya 131 Kanga 1 Kanga 121131001 1 5471 12 Mbeya 1 1 Chunya 131 Kanga 2 Tete 121131002 1 3820 12 Mbeya 1 1 Chunya 141 Galula 1 Galula 121141001 1 4388 12 Mbeya 1 1 Chunya 141 Galula 3 Magamba 121141003 1 4814 12 Mbeya 1 1 Chunya 151 Mbuyuni 1 Mbuyuni 121151001 1 7269 12 Mbeya 1 1 Chunya 151 Mbuyuni 3 Ifuko 121151003 1 2980 12 Mbeya 1 1 Chunya 161 Totowe 2 Iyovyo 121161002 1 2526 12 Mbeya 1 1 Chunya 171 Namkukwe 2 Mheza 121171002 1 1676 12 Mbeya 1 1 Chunya 183 Mkwajuni 2 Iseche 121183002 1 1627 12 Mbeya 1 1 Chunya 183 Mkwajuni 4 Mkwajuni 121183004 1 3175 12 Mbeya 1 1 Chunya 183 Mkwajuni 7 Kaloleni 121183007 1 1534 12 Mbeya 1 1 Chunya 201 Kapalala 2 Udinde/iboma 121201002 1 2448 12 Mbeya 1 1 Chunya 211 Gua 2 Some 121211002 1 602 12 Mbeya 1 1 Chunya 231 Mamba 1 Mamba 121231001 1 6791 12 Mbeya 2 2 Mbeya (R) 11 Ihango 2 Haporoto 122011002 1 2251 12 Mbeya 2 2 Mbeya (R) 23 Ulenje 2 Mbonile 122023002 1 1291 12 Mbeya 2 2 Mbeya (R) 33 Tembela 1 Galijembe 122033001 1 2606 12 Mbeya 2 2 Mbeya (R) 33 Tembela 6 Itambalila 122033006 1 761 12 Mbeya 2 2 Mbeya (R) 41 Ijombe 7 Mwashoma 122041007 1 747 12 Mbeya 2 2 Mbeya (R) 51 Santilya 3 Sanje 122051003 1 2690 12 Mbeya 2 2 Mbeya (R) 51 Santilya 7 Jojo 122051007 1 3109 12 Mbeya 2 2 Mbeya (R) 61 Ilembo 3 Shilanga 122061003 1 1204 12 Mbeya 2 2 Mbeya (R) 61 Ilembo 8 Masoko 122061008 1 3100 12 Mbeya 2 2 Mbeya (R) 61 Ilembo 12 Mbagala 122061012 1 1306 12 Mbeya 2 2 Mbeya (R) 71 Iwiji 3 Izyira 122071003 1 3815 12 Mbeya 2 2 Mbeya (R) 81 Isuto 1 Pashungu 122081001 1 2615 12 Mbeya 2 2 Mbeya (R) 81 Isuto 4 Mlowo 122081004 1 2152 12 Mbeya 2 2 Mbeya (R) 91 Igale 1 Itaga 122091001 1 1149 12 Mbeya 2 2 Mbeya (R) 91 Igale 4 Horongo 122091004 1 1238 12 Mbeya 2 2 Mbeya (R) 91 Igale 8 Swaya 122091008 1 2793 12 Mbeya 2 2 Mbeya (R) 101 Iwindi 1 Iwindi 122101001 1 4994 12 Mbeya 2 2 Mbeya (R) 101 Iwindi 4 Mwampalala 122101004 1 1747 12 Mbeya 2 2 Mbeya (R) 101 Iwindi 8 Mwashiwawala 122101008 1 1620 12 Mbeya 2 2 Mbeya (R) 113 UT/Usongwe 4 Itimba 122113004 1 1531 12 Mbeya 2 2 Mbeya (R) 121 Mshewe 3 Mlele 122121003 1 2137 12 Mbeya 2 2 Mbeya (R) 121 Mshewe 7 Mshewe 122121007 1 1902 12 Mbeya 2 2 Mbeya (R) 153 Bonde la Usongwe 2 Ikumbi 122153002 1 1606 12 Mbeya 2 2 Mbeya (R) 153 Bonde la Usongwe 5 Malowe 122153005 1 3071 12 Mbeya 2 2 Mbeya (R) 161 Inyala 3 Iyawaya 122161003 1 906 12 Mbeya 2 2 Mbeya (R) 171 Ilungu 1 Nyalwela 122171001 1 2395 94 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 12 Mbeya 2 2 Mbeya (R) 171 Ilungu 5 Mwela 122171005 1 1397 12 Mbeya 3 3 Kyela 11 Lusungo 5 Lusungo 123011005 1 2253 12 Mbeya 3 3 Kyela 21 Makwale 3 Sebe 123021003 1 1362 12 Mbeya 3 3 Kyela 21 Makwale 7 Mpunguti 123021007 1 1957 12 Mbeya 3 3 Kyela 31 Matema 1 Mababu 123031001 1 5897 12 Mbeya 3 3 Kyela 31 Matema 2 Matema 123031002 1 3886 12 Mbeya 3 3 Kyela 41 Mwaya 3 Masebe 123041003 1 1196 12 Mbeya 3 3 Kyela 41 Mwaya 6 Lugombo 123041006 1 3735 12 Mbeya 3 3 Kyela 53 Kyela Mjini 2 Nkuyu 123053002 1 1895 12 Mbeya 3 3 Kyela 61 Kajunjumele 1 Kilwa 123061001 1 2321 12 Mbeya 3 3 Kyela 61 Kajunjumele 3 Kiingira 123061003 1 1302 12 Mbeya 3 3 Kyela 71 Bujonde 2 Lubaga 123071002 1 2438 12 Mbeya 3 3 Kyela 81 Ikolo 1 kilasilo 123081001 1 3757 12 Mbeya 3 3 Kyela 81 Ikolo 5 Ikolo 123081005 1 2717 12 Mbeya 3 3 Kyela 91 Katumba Songwe 2 Isaki 123091002 1 978 12 Mbeya 3 3 Kyela 91 Katumba Songwe 6 Katumba - Songwe 123091006 1 2678 12 Mbeya 3 3 Kyela 101 Ngana 3 Ushirika 123101003 1 1139 12 Mbeya 3 3 Kyela 111 Busole 1 Lema 123111001 1 3694 12 Mbeya 3 3 Kyela 111 Busole 4 Busale 123111004 1 3172 12 Mbeya 3 3 Kyela 111 Busole 5 Ibanda 123111005 1 2623 12 Mbeya 3 3 Kyela 121 Ipande 1 Mbula 123121001 1 1786 12 Mbeya 3 3 Kyela 121 Ipande 5 Sinyanga 123121005 1 2792 12 Mbeya 3 3 Kyela 131 Ikama 3 Mpuguti 123131003 1 1085 12 Mbeya 3 3 Kyela 143 Ipinda 1 Mabunga 123143001 1 1628 12 Mbeya 3 3 Kyela 143 Ipinda 5 Ipinda 123143005 1 3565 12 Mbeya 3 3 Kyela 143 Ipinda 7 Ikulu 123143007 1 1467 12 Mbeya 3 3 Kyela 151 Ngonga 2 Itete 123151002 1 1620 12 Mbeya 3 3 Kyela 151 Ngonga 5 Lugombo 123151005 1 1852 12 Mbeya 4 4 Rungwe 21 Katumba 1 Ilinga 124021001 1 2032 12 Mbeya 4 4 Rungwe 21 Katumba 3 Ikama 124021003 1 1674 12 Mbeya 4 4 Rungwe 31 Suma 3 Ibumba 124031003 1 1114 12 Mbeya 4 4 Rungwe 43 Kandete 4 Ipelo 124043004 1 1194 12 Mbeya 4 4 Rungwe 51 Luteba 3 Ikubo 124051003 1 1907 12 Mbeya 4 4 Rungwe 71 Isange 1 Mbigili 124071001 1 2145 12 Mbeya 4 4 Rungwe 71 Isange 4 Matamba 124071004 1 1229 12 Mbeya 4 4 Rungwe 91 Lwangwa 1 Ikama Mbande 124091001 1 3556 12 Mbeya 4 4 Rungwe 101 Rufiryo 3 Kikuba 124101003 1 1014 12 Mbeya 4 4 Rungwe 121 Kisegese 2 Kisegese 124121002 1 1914 12 Mbeya 4 4 Rungwe 131 Lupata 3 Nsoso 124131003 1 1275 12 Mbeya 4 4 Rungwe 141 Kambasegela 6 Kambasegela 124141006 1 1452 12 Mbeya 4 4 Rungwe 151 Masukulu 5 Matwebe 124151005 1 1589 95 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 12 Mbeya 4 4 Rungwe 171 Masoko 2 Bujesi 124171002 1 849 12 Mbeya 4 4 Rungwe 181 Bujela 1 Segela,Katonya 124181001 1 1079 12 Mbeya 4 4 Rungwe 191 Ilima 5 Itula - Chinanjele Tea Estate 124191005 1 1795 12 Mbeya 4 4 Rungwe 201 Kisondela 3 Bugoba - Lusungo 124201003 1 2233 12 Mbeya 4 4 Rungwe 211 Ikuti 4 Ibungu 124211004 1 1129 12 Mbeya 4 4 Rungwe 223 Malindo 3 Ibungila 124223003 1 2826 12 Mbeya 4 4 Rungwe 251 Lufingo 1 Lugombo 124251001 1 3345 12 Mbeya 4 4 Rungwe 251 Lufingo 4 Simike 124251004 1 4434 12 Mbeya 4 4 Rungwe 261 Nkunga 3 Isaka 124261003 1 2445 12 Mbeya 4 4 Rungwe 261 Nkunga 7 Mpombo 124261007 1 817 12 Mbeya 4 4 Rungwe 281 Kinyala 2 Igembe 124281002 1 2347 12 Mbeya 4 4 Rungwe 281 Kinyala 5 Lukata 124281005 1 3278 12 Mbeya 4 4 Rungwe 293 Kiwira 2 Ilundo - Ibaga 'A' 124293002 1 3867 12 Mbeya 4 4 Rungwe 293 Kiwira 5 Mpandapanda 124293005 1 2029 12 Mbeya 5 5 Ileje 13 Itumba 1 Yenzebwe 125013001 1 1211 12 Mbeya 5 5 Ileje 13 Itumba 2 Itumba 125013002 1 4958 12 Mbeya 5 5 Ileje 13 Itumba 3 Mlale - Shigombola 125013003 1 3829 12 Mbeya 5 5 Ileje 21 Itale 2 Itale - Kabwe 125021002 1 3050 12 Mbeya 5 5 Ileje 31 Ibaba 1 Sheyo 125031001 1 1635 12 Mbeya 5 5 Ileje 31 Ibaba 4 Ibaba 125031004 1 1394 12 Mbeya 5 5 Ileje 41 Ndola 3 Igumila 125041003 1 1556 12 Mbeya 5 5 Ileje 51 Luswisi 2 Luswisi 125051002 1 2196 12 Mbeya 5 5 Ileje 61 Ngulilo 1 Ndapwa 125061001 1 844 12 Mbeya 5 5 Ileje 71 Lubanda 1 Mbembati 125071001 1 1273 12 Mbeya 5 5 Ileje 71 Lubanda 3 Mtula 125071003 1 2029 12 Mbeya 5 5 Ileje 81 Ngulugulu 1 Bufula 125081001 1 586 12 Mbeya 5 5 Ileje 81 Ngulugulu 4 Chikumbulu 125081004 1 1732 12 Mbeya 5 5 Ileje 91 Sange 2 Sange 125091002 1 1684 12 Mbeya 5 5 Ileje 101 Ikinga 1 Kikota 125101001 1 2179 12 Mbeya 5 5 Ileje 101 Ikinga 4 Kapeta 125101004 1 930 12 Mbeya 5 5 Ileje 111 Kafule 1 Isoko 125111001 1 1841 12 Mbeya 5 5 Ileje 111 Kafule 4 Kapelekesi 125111004 1 2706 12 Mbeya 5 5 Ileje 121 Malangali 2 Chembe 125121002 1 1486 12 Mbeya 5 5 Ileje 121 Malangali 4 Ilondo 125121004 1 1726 12 Mbeya 5 5 Ileje 121 Malangali 6 Kabale 125121006 1 1589 12 Mbeya 5 5 Ileje 131 Bupigu 3 Bupigu 125131003 1 1607 12 Mbeya 5 5 Ileje 143 Isongole 1 Izuba 125143001 1 2145 12 Mbeya 5 5 Ileje 143 Isongole 3 Ilulu 125143003 1 1513 12 Mbeya 5 5 Ileje 151 Chitete 2 Ikumbilo 125151002 1 2843 12 Mbeya 5 5 Ileje 151 Chitete 3 Msia - Majengo 125151003 1 2214 96 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 12 Mbeya 5 5 Ileje 161 Mbebe 2 Mapogolo 125161002 1 1349 12 Mbeya 6 6 Mbozi 11 Chilulumo 5 Kaonga 126011005 1 2409 12 Mbeya 6 6 Mbozi 21 Kamsamba 4 Mpapa 126021004 1 1733 12 Mbeya 6 6 Mbozi 31 Ivuna 5 Ntungwa 126031005 1 2552 12 Mbeya 6 6 Mbozi 41 Nambinzo 3 Shitunguru 126041003 1 2451 12 Mbeya 6 6 Mbozi 51 Itaka 4 Ipoloto 126051004 1 3624 12 Mbeya 6 6 Mbozi 51 Itaka 7 Insani 126051007 1 4750 12 Mbeya 6 6 Mbozi 61 Isansa 4 Itumpi 126061004 1 3141 12 Mbeya 6 6 Mbozi 61 Isansa 7 Isansa 126061007 1 7464 12 Mbeya 6 6 Mbozi 71 Ruanda 5 Namlonga 126071005 1 1064 12 Mbeya 6 6 Mbozi 81 Iyula 1 Igale 126081001 1 3953 12 Mbeya 6 6 Mbozi 81 Iyula 4 Ilomba 126081004 1 4915 12 Mbeya 6 6 Mbozi 91 Nyimbili 4 Hezya 126091004 1 2965 12 Mbeya 6 6 Mbozi 101 Mlangali 2 Mbewe 126101002 1 2656 12 Mbeya 6 6 Mbozi 111 Myovizi 3 Maenje 126111003 1 4415 12 Mbeya 6 6 Mbozi 121 Igamba 1 Msanyila 126121001 1 5328 12 Mbeya 6 6 Mbozi 131 Halungu 1 Lwati 126131001 1 3079 12 Mbeya 6 6 Mbozi 131 Halungu 4 Halungu 126131004 1 5752 12 Mbeya 6 6 Mbozi 141 Msia 4 Ibembwa 126141004 1 3316 12 Mbeya 6 6 Mbozi 153 Mlowo 1 Mlowo 126153001 1 5638 12 Mbeya 6 6 Mbozi 163 Vwawa 5 Ilembo - Namlela 126163005 1 3332 12 Mbeya 6 6 Mbozi 171 Isandula 4 Chizumbi 126171004 1 1454 12 Mbeya 6 6 Mbozi 181 Ihanda 7 Shilanga 126181007 1 1804 12 Mbeya 6 6 Mbozi 181 Ihanda 10 Sakamwela 126181010 1 2678 12 Mbeya 6 6 Mbozi 211 Msangano 2 Msangano 126211002 1 4095 12 Mbeya 6 6 Mbozi 221 Chitete 2 Chitete 126221002 1 3912 12 Mbeya 6 6 Mbozi 241 Kapele 5 Kapele 126241005 1 1541 12 Mbeya 6 6 Mbozi 261 Nkangamo 1 Isanga 126261001 1 2034 12 Mbeya 7 7 Mbarali 21 Madibira 2 Nyamakuyu 127021002 1 2171 12 Mbeya 7 7 Mbarali 21 Madibira 3 Mkunywa 127021003 1 6045 12 Mbeya 7 7 Mbarali 21 Madibira 4 Mahango 'A' 127021004 1 5787 12 Mbeya 7 7 Mbarali 21 Madibira 8 Mapogoro 127021008 1 6147 12 Mbeya 7 7 Mbarali 31 Mawindi 1 Igunda 127031001 1 5932 12 Mbeya 7 7 Mbarali 31 Mawindi 6 Kangaga 127031006 1 2599 12 Mbeya 7 7 Mbarali 31 Mawindi 9 Isunura 127031009 1 4880 12 Mbeya 7 7 Mbarali 43 Rujewa 1 Nyeregete 127043001 1 4911 12 Mbeya 7 7 Mbarali 43 Rujewa 4 Uhamila 127043004 1 2453 12 Mbeya 7 7 Mbarali 43 Rujewa 8 Kanioga 127043008 1 1242 12 Mbeya 7 7 Mbarali 51 Mapogoro 4 Madabaga 127051004 1 3187 12 Mbeya 7 7 Mbarali 51 Mapogoro 6 Uturo 127051006 1 3633 12 Mbeya 7 7 Mbarali 63 Chimala 4 Kapunga 127063004 1 3024 97 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 12 Mbeya 7 7 Mbarali 63 Chimala 7 Muwale 127063007 1 1788 12 Mbeya 7 7 Mbarali 71 Utengule/Usangu 1 Madundasi 127071001 1 4655 12 Mbeya 7 7 Mbarali 71 Utengule/Usangu 6 Mpolo 127071006 1 3406 12 Mbeya 7 7 Mbarali 71 Utengule/Usangu 8 Mahango 127071008 1 1619 12 Mbeya 7 7 Mbarali 81 Ruiwa 1 Wimba Mahango 127081001 1 3750 12 Mbeya 7 7 Mbarali 81 Ruiwa 5 Ijumbi 127081005 1 2190 12 Mbeya 7 7 Mbarali 91 Mahongole 3 Ilongo 127091003 1 2864 12 Mbeya 7 7 Mbarali 91 Mahongole 5 Mhwela 127091005 1 1828 12 Mbeya 7 7 Mbarali 103 Ubaruku 1 Mwanavala 127103001 1 2694 12 Mbeya 7 7 Mbarali 103 Ubaruku 3 Urunda 127103003 1 3262 12 Mbeya 7 7 Mbarali 103 Ubaruku 6 Ibohora 127103006 1 978 12 Mbeya 7 7 Mbarali 113 Igurusi 2 Kongolo Mswiswi 127113002 1 3771 12 Mbeya 7 7 Mbarali 113 Igurusi 5 Igurusi 127113005 1 2732 12 Mbeya 7 7 Mbarali 113 Igurusi 7 Mambi 127113007 1 954 12 Mbeya 8 8 Mbeya U 33 Iganzo 1 Igodima 128033001 1 858 12 Mbeya 8 8 Mbeya U 41 Mwansenkwa 1 Ilembo & Mengo 128041001 1 1349 12 Mbeya 8 8 Mbeya U 51 Itagano 1 Ipombo & Itagano 128051001 1 1232 12 Mbeya 8 8 Mbeya U 63 Itezi 1 Gombe Kusini 128063001 1 2208 12 Mbeya 8 8 Mbeya U 73 Nsalaga 1 Itezi Mlimani 128073001 1 1058 12 Mbeya 8 8 Mbeya U 83 Igawilo 1 Mponja 128083001 1 776 12 Mbeya 8 8 Mbeya U 93 Iganjo 1 Itanji 128093001 1 1648 12 Mbeya 8 8 Mbeya U 103 Uyole 1 Iwambala 128103001 1 1781 12 Mbeya 8 8 Mbeya U 113 Iduda 1 Mwahala 128113001 1 1322 12 Mbeya 8 8 Mbeya U 121 Mwasanga 1 Mwasanga & Isoso 128121001 1 544 12 Mbeya 8 8 Mbeya U 131 Tembela 1 Tembela & Reli 128131001 1 989 12 Mbeya 8 8 Mbeya U 143 Ilomba 1 Ituha 128143001 1 2695 12 Mbeya 8 8 Mbeya U 153 Mwakibete 1 Bomba Mbili 128153001 1 2837 12 Mbeya 8 8 Mbeya U 163 Ilemi 1 Ilindi 128163001 1 482 12 Mbeya 8 8 Mbeya U 173 Isyesye 1 Mwantengule 128173001 1 1244 12 Mbeya 8 8 Mbeya U 193 Iyela 1 Iyela I 128193001 1 1368 12 Mbeya 8 8 Mbeya U 263 Kalobe 1 DDC 128263001 1 1380 12 Mbeya 8 8 Mbeya U 273 Iyunga 1 Inyala 128273001 1 1178 12 Mbeya 8 8 Mbeya U 273 Iyunga 2 Sisitila 128273002 1 734 12 Mbeya 8 8 Mbeya U 273 Iyunga 3 Igale 128273003 1 399 12 Mbeya 8 8 Mbeya U 283 Iwambi 1 Mayombo 128283001 1 762 12 Mbeya 8 8 Mbeya U 291 Itende 1 Lusungo 128291001 1 2715 12 Mbeya 8 8 Mbeya U 301 Iziwa 1 Iziwa 128301001 1 2941 12 Mbeya 8 8 Mbeya U 313 Nsoho 1 Nsoho 128313001 1 736 13 Singida 1 1 Iramba 13 Kiomboi 5 Mampanta - Kibaoni,Milangali ' 131013005 1 1837 98 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 13 Singida 1 1 Iramba 31 Tulya 3 Doromoni - Kiteka,Doromoni 131031003 1 1741 13 Singida 1 1 Iramba 51 Mpambala 1 Nyahaa - Mwamakona,Msongona 131051001 1 3333 13 Singida 1 1 Iramba 63 Ibaga 2 Mkalama - Jengela Ngulu,Mtapwa 131063002 1 2483 13 Singida 1 1 Iramba 71 Mwangeza 5 Mwangeza - Dar,Issene 131071005 1 2629 13 Singida 1 1 Iramba 81 Nkinto 4 Kinyambuli 131081004 1 2166 13 Singida 1 1 Iramba 91 Mwanga 2 Kidarafa,Msisai 'A' 131091002 1 3804 13 Singida 1 1 Iramba 91 Mwanga 5 Nkalankala - Mwangaza,Lumumba 131091005 1 4605 13 Singida 1 1 Iramba 101 Ilunda 2 Singa - Majengo Mapya,Gereza 131101002 1 4118 13 Singida 1 1 Iramba 101 Ilunda 5 Iambi - Igaya 131101005 1 2441 13 Singida 1 1 Iramba 121 Gumanga 1 Kisuluiga - Mkukuma 131121001 1 2356 13 Singida 1 1 Iramba 131 Msingi 1 Msingi - Mkusi,Kidii 131131001 1 2802 13 Singida 1 1 Iramba 141 Kinyangili 2 Yulansoni - Mtandua 131141002 1 2778 13 Singida 1 1 Iramba 141 Kinyangili 5 Kikhonda 131141005 1 4967 13 Singida 1 1 Iramba 153 Iguguno 4 Senene - Mbuyuni 131153004 1 2590 13 Singida 1 1 Iramba 161 Kinampanda 3 Uwanza - Mtumbili 131161003 1 2206 13 Singida 1 1 Iramba 171 Kyengege 1 Kyengege 131171001 1 2781 13 Singida 1 1 Iramba 181 Kaselya 3 Nsonga - Kipimbi,Kati 131181003 1 2202 13 Singida 1 1 Iramba 191 Mbelekese 1 Misuna - Kilambazi 131191001 1 3513 13 Singida 1 1 Iramba 211 Urughu 2 Masimba - Mayanzani,Makio 131211002 1 2623 13 Singida 1 1 Iramba 221 Mtekente 3 Msansao 131221003 1 5548 13 Singida 1 1 Iramba 231 Ulemo 2 Ulemo - Majengo,Mpuli 131231002 1 2803 13 Singida 1 1 Iramba 241 Mtoa 1 Masagi - Miembeni 131241001 1 3987 13 Singida 1 1 Iramba 241 Mtoa 4 Mtoa - Mpambaa 131241004 1 3367 13 Singida 1 1 Iramba 253 Shelui 3 Mseko 'A & B' 131253003 1 2648 13 Singida 1 1 Iramba 261 Ntwike 2 Shelui II - Kyulungi 131261002 1 1950 13 Singida 1 1 Iramba 261 Ntwike 5 Nsunsu - Ngwasangasa 131261005 1 3309 13 Singida 2 2 Singida Ru 11 Ughandi 1 Senene Mfuru 132011001 1 1568 13 Singida 2 2 Singida Ru 23 Mtinko 1 Mpambaa - Kafanabo,Utemini 132023001 1 2804 13 Singida 2 2 Singida Ru 23 Mtinko 5 Ikiwu - Nkwae,Muungano 132023005 1 4539 13 Singida 2 2 Singida Ru 31 Makuro 5 Ng'ong'oampoku 132031005 1 3303 13 Singida 2 2 Singida Ru 43 Ilongero 3 Itamka - Mwayadi,Unyarughe- Kim 132043003 1 2293 99 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 13 Singida 2 2 Singida Ru 51 Ikhanoda 5 Msimihi - Mbiru 132051005 1 3694 13 Singida 2 2 Singida Ru 61 Maghojoa 2 Sefunga - Mijohu 132061002 1 3342 13 Singida 2 2 Singida Ru 71 Merya 3 Kinyagigi - Milungu 132071003 1 2559 13 Singida 2 2 Singida Ru 81 Kinyeto 3 Mkimbi - Maumbahi,Mtoghoo 132081003 1 1990 13 Singida 2 2 Singida Ru 91 Ngimu 3 Mwiganji - Maswele 132091003 1 2494 13 Singida 2 2 Singida Ru 101 Mgori 1 Ndughamughanga - Makulu 132101001 1 1847 13 Singida 2 2 Singida Ru 111 Siuyu 4 Makotea 132111004 1 1471 13 Singida 2 2 Singida Ru 133 Mungaa 1 Kinku - Makoteo 132133001 1 2322 13 Singida 2 2 Singida Ru 141 Ntuntu 4 Ntewa - Azimio,Kujitegemea 132141004 1 3974 13 Singida 2 2 Singida Ru 151 Mangonyi 3 Sambaru - Kipompo 132151003 1 1910 13 Singida 2 2 Singida Ru 173 Ikungu 3 Mahambe - Kati,Kinyangaa 132173003 1 1291 13 Singida 2 2 Singida Ru 173 Ikungu 4 Matare - Makhambi 132173004 1 4201 13 Singida 2 2 Singida Ru 193 Puma 4 Nkuninkana - 132193004 1 2061 13 Singida 2 2 Singida Ru 201 Ihanja 1 Unyangwe - Ufivu,Unyinkungu 132201001 1 2066 13 Singida 2 2 Singida Ru 211 Minyughe 3 Minyughe 132211003 1 2834 13 Singida 2 2 Singida Ru 221 Muhintiri 2 Muhintiri 132221002 1 3257 13 Singida 2 2 Singida Ru 231 Mgungira 2 Mgungira 132231002 1 2721 13 Singida 2 2 Singida Ru 241 Mwaru 2 Mpunguzi 132241002 1 2293 13 Singida 2 2 Singida Ru 251 Sepuka 3 Kintandaa - Mayuyuda 'B' 132251003 1 3709 13 Singida 2 2 Singida Ru 251 Sepuka 5 Msimi 132251005 1 4940 13 Singida 2 2 Singida Ru 271 Msisi 3 Msisi 132271003 1 1845 13 Singida 2 2 Singida Ru 281 Mudida 3 Migugu 132281003 1 2994 13 Singida 3 3 Manyoni 13 Manyoni 1 Mpamaa - Majengo,Mkwese 133013001 1 2626 13 Singida 3 3 Manyoni 13 Manyoni 5 Muhalala 133013005 1 2022 13 Singida 3 3 Manyoni 23 Kilimatinde 2 Solya - Msangalale,Kinangali 133023002 1 1909 13 Singida 3 3 Manyoni 31 Makuru 2 Makuru - Chang'ombe 133031002 1 3710 13 Singida 3 3 Manyoni 31 Makuru 4 Saranda 133031004 1 4775 13 Singida 3 3 Manyoni 41 Chikuyu 2 Makutopora - Walanchinza 133041002 1 3659 13 Singida 3 3 Manyoni 51 Makanda 1 Magasai - Magasai 'B',Chisugar 133051001 1 2048 13 Singida 3 3 Manyoni 51 Makanda 3 Kitalalo 133051003 1 1995 13 Singida 3 3 Manyoni 63 Kintinku 2 Lusilile - Waraka,Reli & Ifad 133063002 1 3654 100 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 13 Singida 3 3 Manyoni 71 Maweni 3 Ngaiti 133071003 1 3364 13 Singida 3 3 Manyoni 81 Majiri 3 Majiri - Isuhula 133081003 1 4093 13 Singida 3 3 Manyoni 91 Sasajira 1 Chibumagwa - Msakile 133091001 1 3647 13 Singida 3 3 Manyoni 101 Idodyandole 2 Idodyandole 133101002 1 3881 13 Singida 3 3 Manyoni 111 Chikola 1 Chikola - Itetema 133111001 1 4503 13 Singida 3 3 Manyoni 121 Heka - Azimio 1 Haka Azimio - Heka ya Kati 133121001 1 4642 13 Singida 3 3 Manyoni 121 Heka - Azimio 3 Sasilo - Mwitikila 133121003 1 7656 13 Singida 3 3 Manyoni 131 Nkonko 1 Mpula - Imalamakuo 133131001 1 2489 13 Singida 3 3 Manyoni 131 Nkonko 3 Nkonko 133131003 1 2266 13 Singida 3 3 Manyoni 141 Sanza 3 Sanza - Mchetye 133141003 1 2875 13 Singida 3 3 Manyoni 151 Isseke 2 Igwamadete - Wota & Chandama 133151002 1 3551 13 Singida 3 3 Manyoni 161 Rungwa 1 Mwamagembe - Mabatini,Isingiwe 133161001 1 1524 13 Singida 3 3 Manyoni 173 Mgandu 2 Mitundu 133173002 1 5409 13 Singida 3 3 Manyoni 173 Mgandu 3 Makale 133173003 1 2968 13 Singida 3 3 Manyoni 173 Mgandu 4 Kayui 133173004 1 4375 13 Singida 3 3 Manyoni 183 Itigi 2 Dorotto - Ipunguli,Kahomwe,Msi 133183002 1 1654 13 Singida 3 3 Manyoni 191 Ipande 3 Damwelu 133191003 1 1164 13 Singida 3 3 Manyoni 211 Aghondi 1 Kamenyanga 133211001 1 1766 13 Singida 4 4 Singida Ur 11 Mtipa 1 Manga 134011001 1 3130 13 Singida 4 4 Singida Ur 11 Mtipa 2 Mtipa - Nyunjui,Mwendapole 134011002 1 3380 13 Singida 4 4 Singida Ur 51 Unyambwa 1 Kisasida - Ifungi 134051001 1 3648 13 Singida 4 4 Singida Ur 51 Unyambwa 2 Unyambwa/Sanga/Maach ie 134051002 1 3686 13 singida 4 4 Singida Ur 71 Unyamikumbi 1 Unyaminkumbi-''A'' 134071001 1 1615 13 Singida 4 4 Singida Ur 71 Unyamikumbi 2 Ughaugha ''A'' 134071002 1 2194 13 Singida 4 4 Singida Ur 71 Unyamikumbi 3 Ughaugha ''B'' 134071003 1 993 13 Singida 4 4 Singida Ur 71 Unyamikumbi 4 Unyamikumbi-''B'' 134071004 1 2242 13 Singida 4 4 Singida Ur 71 Unyamikumbi 5 Kisaki - Iraonyehe 134071005 1 3459 13 Singida 4 4 Singida Ur 81 Mtamaa 1 Mtamaa 'B' - Mnyituka 134081001 1 3481 13 Singida 4 4 Singida Ur 81 Mtamaa 2 Mtamaa 'A' - Mkwio 134081002 1 2769 13 Singida 4 4 Singida Ur 121 Mwankoko 1 Mwankoko 'A' - Darajani,Mwachi 134121001 1 2835 13 Singida 4 4 Singida Ur 121 Mwankoko 2 Mwankoko 'B' - Mughumo 134121002 1 2316 13 Singida 4 4 Singida Ur 121 Mwankoko 3 Unyunga - Mwajakhoma 134121003 1 3598 13 Singida 4 4 Singida Ur 131 Mandewa 1 Ititi - Mbuyuni 134131001 1 2053 101 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 13 Singida 4 4 Singida Ur 131 Mandewa 2 Uhamaka/Mgondwe 134131002 1 2562 13 Singida 4 4 Singida Ur 131 Mandewa 3 Minjuki - Njuki,Mikaratusi 134131003 1 2089 13 Singida 4 4 Singida Ur 131 Mandewa 4 Mandewa - Mamise 134131004 1 7879 14 Tabora 1 1 Nzega 11 Puge 1 Busondo 141011001 1 5197 14 Tabora 1 1 Nzega 21 Nkiniziwa 2 Nkiniziwa 141021002 1 3903 14 Tabora 1 1 Nzega 31 Budushi 1 Budushi 141031001 1 2642 14 Tabora 1 1 Nzega 41 Mwakanshahala 3 Kigandu 141041003 1 6129 14 Tabora 1 1 Nzega 51 Tongi 5 Ndekeli 141051005 1 4585 14 Tabora 1 1 Nzega 81 Magengati 3 Usagari 141081003 1 1694 14 Tabora 1 1 Nzega 91 Ndala 3 Uhemeli 141091003 1 4730 14 Tabora 1 1 Nzega 121 Mbogwe 2 Mbogwe 141121002 1 3151 14 Tabora 1 1 Nzega 131 Miguwa 6 Kitangili 141131006 1 2295 14 Tabora 1 1 Nzega 141 Itilo 3 Iyombo 141141003 1 4340 14 Tabora 1 1 Nzega 151 Muhugi 3 Nhumbili 141151003 1 3249 14 Tabora 1 1 Nzega 161 Utwigu 5 Ishiki 141161005 1 3726 14 Tabora 1 1 Nzega 181 Nzega Ndogo 1 Zogolo 141181001 1 4019 14 Tabora 1 1 Nzega 191 Lusu 4 Mwaluzwilo 141191004 1 4490 14 Tabora 1 1 Nzega 211 Isanzu 2 Isanzu 141211002 1 2663 14 Tabora 1 1 Nzega 221 Itobo 2 Itobo 141221002 1 3531 14 Tabora 1 1 Nzega 231 Mwangoye 3 Sagida 141231003 1 2463 14 Tabora 1 1 Nzega 241 Sigili 2 Sigili 141241002 1 4094 14 Tabora 1 1 Nzega 251 Mwamala 4 Chaming'hwa 141251004 1 1497 14 Tabora 1 1 Nzega 261 Igusule 2 Ilalo 141261002 1 2995 14 Tabora 1 1 Nzega 281 Kasela 1 Nindo 141281001 1 2284 14 Tabora 1 1 Nzega 291 Karitu 2 Itunda 141291002 1 3575 14 Tabora 1 1 Nzega 311 Mogwa 2 Mogwa 141311002 1 6565 14 Tabora 1 1 Nzega 321 Mambali 4 Kikonoka 141321004 1 3537 14 Tabora 1 1 Nzega 331 Kahamanhalanga 4 Nhabala 141331004 1 4121 14 Tabora 1 1 Nzega 351 Semembela 2 Kasanga 141351002 1 4453 14 Tabora 1 1 Nzega 371 Ikindwa 1 Malolo 141371001 1 3651 14 Tabora 2 2 Igunga 13 Igunga 1 Isugilo 142013001 1 3733 14 Tabora 2 2 Igunga 13 Igunga 4 Mwanzugi 142013004 1 7314 14 Tabora 2 2 Igunga 21 Itumba 2 Lugubu 142021002 1 5026 14 Tabora 2 2 Igunga 31 Bukoko 3 Ipumbulya 142031003 1 3955 14 Tabora 2 2 Igunga 41 Isakamaliwa 3 Hindishi 142041003 1 1752 14 Tabora 2 2 Igunga 61 Nanga 1 Nanga 142061001 1 5591 14 Tabora 2 2 Igunga 61 Nanga 3 Igogo 142061003 1 2845 14 Tabora 2 2 Igunga 71 Nguvumoja 2 Mwalala 142071002 1 3987 14 Tabora 2 2 Igunga 81 Mbutu 2 Bukama 142081002 1 4302 14 Tabora 2 2 Igunga 91 Kining'inila 2 Mwanyagula 142091002 1 3112 102 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 14 Tabora 2 2 Igunga 111 Mwamashimba 1 Mwamashimba 142111001 1 4395 14 Tabora 2 2 Igunga 111 Mwamashimba 4 Mwamakona 142111004 1 3731 14 Tabora 2 2 Igunga 121 Kinungu 4 Mwajilunga 142121004 1 1291 14 Tabora 2 2 Igunga 141 Itunduru 1 Itunduru 142141001 1 4129 14 Tabora 2 2 Igunga 151 Mwamashiga 3 Migongwa 142151003 1 2978 14 Tabora 2 2 Igunga 163 Choma 3 Bulangamilwa 142163003 1 4331 14 Tabora 2 2 Igunga 171 Mwashiku 4 Buchenjegele 142171004 1 3959 14 Tabora 2 2 Igunga 181 Ziba 2 Iborogero 142181002 1 5294 14 Tabora 2 2 Igunga 191 Ndembezi 1 Ndembezi 142191001 1 5489 14 Tabora 2 2 Igunga 201 Nkinga 1 Ulaya 142201001 1 3127 14 Tabora 2 2 Igunga 201 Nkinga 4 Nkinga 142201004 1 7254 14 Tabora 2 2 Igunga 211 Ngulu 1 Ngulu 142211001 1 1958 14 Tabora 2 2 Igunga 221 Simbo 2 Tambalale 142221002 1 3342 14 Tabora 2 2 Igunga 231 Igoweko 2 Igoweko 142231002 1 4833 14 Tabora 2 2 Igunga 241 Mwisi 1 Isenegeja 142241001 1 2688 14 Tabora 2 2 Igunga 251 Chabutwa 1 Majengo 142251001 1 2097 14 Tabora 2 2 Igunga 261 Sungwizi 2 Mwamala 142261002 1 3851 14 Tabora 3 3 Uyui 11 Lutende 1 Lutende 143011001 1 5610 14 Tabora 3 3 Uyui 11 Lutende 4 Mwisole 143011004 1 11623 14 Tabora 3 3 Uyui 21 Kizengi 1 Kizengi 143021001 1 5838 14 Tabora 3 3 Uyui 21 Kizengi 5 Karangasi 143021005 1 1192 14 Tabora 3 3 Uyui 31 Goweko 2 Goweko 143031002 1 5787 14 Tabora 3 3 Uyui 31 Goweko 4 Nsotolo 143031004 1 6951 14 Tabora 3 3 Uyui 41 Igalula 1 Kigwa 'B' 143041001 1 8339 14 Tabora 3 3 Uyui 41 Igalula 5 Igalula 143041005 1 3517 14 Tabora 3 3 Uyui 53 Ilolangulu 3 Isila 143053003 1 2457 14 Tabora 3 3 Uyui 63 Mabama 1 Kalola 143063001 1 2350 14 Tabora 3 3 Uyui 63 Mabama 5 Ideka 143063005 1 1085 14 Tabora 3 3 Uyui 71 Ndono 3 Ndono 143071003 1 3102 14 Tabora 3 3 Uyui 81 Ufuluma 3 Makazi 143081003 1 2380 14 Tabora 3 3 Uyui 81 Ufuluma 5 Chessa 143081005 1 2594 14 Tabora 3 3 Uyui 91 Usagali 3 Azimio 143091003 1 2076 14 Tabora 3 3 Uyui 101 Ibiri 4 Isumu 143101004 1 713 14 Tabora 3 3 Uyui 111 Bukumbi 1 Ikami Kalole 143111001 1 5029 14 Tabora 3 3 Uyui 111 Bukumbi 3 Igilimba 143111003 1 7264 14 Tabora 3 3 Uyui 111 Bukumbi 4 Ishihimulwa 143111004 1 8684 14 Tabora 3 3 Uyui 121 Ikongolo 4 Kanyenye 143121004 1 1603 14 Tabora 3 3 Uyui 131 Upuge 4 Kasenga 143131004 1 957 14 Tabora 3 3 Uyui 141 Magiri 2 Imalampaka 143141002 1 4125 14 Tabora 3 3 Uyui 141 Magiri 5 Nsimbo 143141005 1 3465 14 Tabora 3 3 Uyui 151 Isikizya 1 Ilalwansimba 143151001 1 3417 103 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 14 Tabora 3 3 Uyui 151 Isikizya 4 Igoko 143151004 1 3152 14 Tabora 3 3 Uyui 161 Shitage 2 Nyangalamila 143161002 1 4771 14 Tabora 3 3 Uyui 171 Loya 2 Loya 143171002 1 5898 14 Tabora 4 4 Urambo 21 Imalamakoye 3 Itebulanda 144021003 1 4482 14 Tabora 4 4 Urambo 31 Muungano 2 Muungano 144031002 1 4358 14 Tabora 4 4 Urambo 41 Itundu 2 Mpigwa 144041002 1 2525 14 Tabora 4 4 Urambo 41 Itundu 5 Wema 144041005 1 3235 14 Tabora 4 4 Urambo 51 Songambele 2 Songambele 144051002 1 3095 14 Tabora 4 4 Urambo 51 Songambele 4 Igunguli 144051004 1 6010 14 Tabora 4 4 Urambo 61 Ukondamoyo 4 Kamalendi 144061004 1 1803 14 Tabora 4 4 Urambo 81 Kapilula 1 Kapilula 144081001 1 1313 14 Tabora 4 4 Urambo 101 Uyumbu 2 Izimbili 144101002 1 3131 14 Tabora 4 4 Urambo 121 Usisya 4 Sipungu - Chekeleni 144121004 1 2005 14 Tabora 4 4 Urambo 141 Kashishi 1 Kashishi 144141001 1 9029 14 Tabora 4 4 Urambo 141 Kashishi 2 Seleli 144141002 1 6621 14 Tabora 4 4 Urambo 141 Kashishi 5 King'wamgoko 144141005 1 7960 14 Tabora 4 4 Urambo 141 Kashishi 7 Nyasa 144141007 1 6186 14 Tabora 4 4 Urambo 161 Mwongozo 1 Mwongozo 144161001 1 2727 14 Tabora 4 4 Urambo 171 Kanindo 1 Kanindo 144171001 1 5691 14 Tabora 4 4 Urambo 171 Kanindo 3 Mbeta 144171003 1 4690 14 Tabora 4 4 Urambo 181 Milambo 1 Ikonongo 144181001 1 5827 14 Tabora 4 4 Urambo 191 Igombe Mkulu 3 Keza 144191003 1 2475 14 Tabora 4 4 Urambo 211 Ushokola 3 Pozamoyo 144211003 1 1089 14 Tabora 4 4 Urambo 221 Kazaroho 2 Igwisi 144221002 1 5342 14 Tabora 4 4 Urambo 221 Kazaroho 5 Nsimbo 144221005 1 2006 14 Tabora 4 4 Urambo 231 Igagala 2 Kazana upate 144231002 1 1384 14 Tabora 4 4 Urambo 231 Igagala 5 Kamsekwa 144231005 1 3093 14 Tabora 4 4 Urambo 241 Usinge 1 Usinge 144241001 1 8131 14 Tabora 4 4 Urambo 251 Ukumbisiganga 1 Ukumbisiganga 144251001 1 3084 14 Tabora 4 4 Urambo 251 Ukumbisiganga 5 Zugimlole 144251005 1 8333 14 Tabora 5 5 Sikonge 11 Tutuo 1 Tutuo 145011001 1 6342 14 Tabora 5 5 Sikonge 11 Tutuo 2 Mitowo 145011002 1 3053 14 Tabora 5 5 Sikonge 11 Tutuo 3 Mole 145011003 1 4989 14 Tabora 5 5 Sikonge 11 Tutuo 4 Usanganya 145011004 1 4661 14 Tabora 5 5 Sikonge 21 Chabutwa 1 Chabutwa 145021001 1 1320 14 Tabora 5 5 Sikonge 21 Chabutwa 2 Kikungu 145021002 1 1433 14 Tabora 5 5 Sikonge 21 Chabutwa 5 Mitwigu 145021005 1 1236 14 Tabora 5 5 Sikonge 31 Kiloleli 2 Kiloleli 145031002 1 3625 14 Tabora 5 5 Sikonge 41 Kipanga 2 Isanjandugu 145041002 1 904 14 Tabora 5 5 Sikonge 41 Kipanga 4 Imalampaka 145041004 1 1754 14 Tabora 5 5 Sikonge 41 Kipanga 6 Ukondamoyo 145041006 1 1654 104 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 14 Tabora 5 5 Sikonge 53 Sikonge 2 Mkolye 145053002 1 3105 14 Tabora 5 5 Sikonge 53 Sikonge 4 Mwamayunga 145053004 1 4705 14 Tabora 5 5 Sikonge 53 Sikonge 6 Mlogolo 145053006 1 1384 14 Tabora 5 5 Sikonge 61 Igigwa 1 Igigwa 145061001 1 6724 14 Tabora 5 5 Sikonge 61 Igigwa 4 Lufisi 145061004 1 2606 14 Tabora 5 5 Sikonge 61 Igigwa 5 Nyahua 145061005 1 3636 14 Tabora 5 5 Sikonge 71 Kitunda 1 Mgambo 145071001 1 2464 14 Tabora 5 5 Sikonge 71 Kitunda 2 Mwenge 145071002 1 3333 14 Tabora 5 5 Sikonge 81 Kiloli 1 Mwitiko 145081001 1 1111 14 Tabora 5 5 Sikonge 91 Kipili 1 Zugimlole 145091001 1 7501 14 Tabora 5 5 Sikonge 91 Kipili 2 Zugimlole II 145091002 1 2097 14 Tabora 5 5 Sikonge 91 Kipili 3 Kikumbi 145091003 1 2236 14 Tabora 5 5 Sikonge 101 Pangale 2 Majengo 145101002 1 2077 14 Tabora 5 5 Sikonge 101 Pangale 5 Mpombwe 145101005 1 3126 14 Tabora 5 5 Sikonge 111 Ipole 2 Ipole 145111002 1 2643 14 Tabora 5 5 Sikonge 111 Ipole 4 Idekamiso 145111004 1 1532 14 Tabora 6 6 Tabora Urb 33 Mbugani 1 usule 146033001 1 278 14 Tabora 6 6 Tabora Urb 123 Ng'ambo 1 Tukutuku 146123001 1 1431 14 Tabora 6 6 Tabora Urb 133 Malolo 1 Mtakuja/Usenge 146133001 1 790 14 Tabora 6 6 Tabora Urb 141 Kakola 1 Kakola 146141001 1 1692 14 Tabora 6 6 Tabora Urb 141 Kakola 2 Magoweko 146141002 1 1768 14 Tabora 6 6 Tabora Urb 141 Kakola 3 Igombe 146141003 1 4891 14 Tabora 6 6 Tabora Urb 151 Uyui 1 Kalumwa 146151001 1 1107 14 Tabora 6 6 Tabora Urb 151 Uyui 2 Imalamihayo 146151002 1 2285 14 Tabora 6 6 Tabora Urb 151 Uyui 3 Uyui 146151003 1 2828 14 Tabora 6 6 Tabora Urb 161 Itonjanda 1 Kazima 146161001 1 1246 14 Tabora 6 6 Tabora Urb 161 Itonjanda 2 Itonjanda 146161002 1 2626 14 Tabora 6 6 Tabora Urb 161 Itonjanda 3 Ifucha 146161003 1 1967 14 Tabora 6 6 Tabora Urb 171 Ndevelwa 1 Inara 146171001 1 3311 14 Tabora 6 6 Tabora Urb 171 Ndevelwa 2 Ndevelwa 146171002 1 3276 14 Tabora 6 6 Tabora Urb 171 Ndevelwa 3 Itulu 146171003 1 2511 14 Tabora 6 6 Tabora Urb 181 Itetemia 1 Itetemia 146181001 1 4270 14 Tabora 6 6 Tabora Urb 181 Itetemia 2 Lusangi 146181002 1 1636 14 Tabora 6 6 Tabora Urb 181 Itetemia 3 Ntalikwa 146181003 1 2185 14 Tabora 6 6 Tabora Urb 193 Tumbi 1 Tumbi 146193001 1 3781 14 Tabora 6 6 Tabora Urb 201 Kalunde 1 Kalunde 146201001 1 4038 14 Tabora 6 6 Tabora Urb 201 Kalunde 2 Izimbili 146201002 1 829 14 Tabora 6 6 Tabora Urb 201 Kalunde 3 Ulamba 146201003 1 765 14 Tabora 6 6 Tabora Urb 211 Misha 1 Kabila 146211001 1 2265 14 Tabora 6 6 Tabora Urb 211 Misha 2 Misha 146211002 1 830 14 Tabora 6 6 Tabora Urb 211 Misha 3 Masagala 146211003 1 2060 105 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 14 Tabora 6 6 Tabora Urb 211 Misha 4 Itaga 146211004 1 1599 14 Tabora 6 6 Tabora Urb 211 Misha 5 Igambiro 146211005 1 2988 15 Rukwa 1 1 Mpanda 11 Kasokola 2 Sungamila 151011002 0 714 15 Rukwa 1 1 Mpanda 11 Kasokola 3 Kasokola 151011003 1 1599 15 Rukwa 1 1 Mpanda 53 Inyonga 2 Nsenkwa 151053002 1 2165 15 Rukwa 1 1 Mpanda 53 Inyonga 4 Inyonga 151053004 0 1251 15 Rukwa 1 1 Mpanda 71 Ilela 3 Mapili 151071003 1 2050 15 Rukwa 1 1 Mpanda 81 Utende 4 Mgombe - Mapambano 151081004 0 1005 15 Rukwa 1 1 Mpanda 91 Mamba 2 kilida 151091002 1 4659 15 Rukwa 1 1 Mpanda 91 Mamba 4 Mamba I 151091004 1 7410 15 Rukwa 1 1 Mpanda 101 Mbede 1 Mwamapuli I 151101001 1 6640 15 Rukwa 1 1 Mpanda 111 Urwira 1 Urwira 151111001 1 2736 15 Rukwa 1 1 Mpanda 121 Nsimbo 2 Isanjandugu 151121002 0 2238 15 Rukwa 1 1 Mpanda 121 Nsimbo 5 Mtakuja 151121005 1 1128 15 Rukwa 1 1 Mpanda 141 Sitalike 1 Sitalike 151141001 0 3277 15 Rukwa 1 1 Mpanda 141 Sitalike 2 Matandalani 151141002 1 1616 15 Rukwa 1 1 Mpanda 153 Usevya 2 Ikuba 151153002 1 5517 15 Rukwa 1 1 Mpanda 171 Machimboni 1 Ibindi 151171001 1 2594 15 Rukwa 1 1 Mpanda 171 Machimboni 3 Dirifu 151171003 0 1441 15 Rukwa 1 1 Mpanda 171 Machimboni 4 Kapanda 151171004 1 1231 15 Rukwa 1 1 Mpanda 183 karema 3 Kapalamsenga 151183003 1 3984 15 Rukwa 1 1 Mpanda 191 Ikola 2 Isengule 151191002 0 2914 15 Rukwa 1 1 Mpanda 191 Ikola 4 Ikola I 151191004 1 6895 15 Rukwa 1 1 Mpanda 201 Kabungu 4 Kabungu 151201004 1 5845 15 Rukwa 1 1 Mpanda 211 Mwese 1 Lwega - Igalula 151211001 0 2097 15 Rukwa 1 1 Mpanda 223 Mishamo 9 Kapemba 151223009 1 2730 15 Rukwa 1 1 Mpanda 231 Katuma 1 Sibwesa 151231001 1 2903 15 Rukwa 1 1 Mpanda 241 Mpanda Ndogo 1 Majalila 151241001 0 4149 15 Rukwa 1 1 Mpanda 241 Mpanda Ndogo 3 Igagala 151241003 1 1078 15 Rukwa 2 2 Sumbawanga 11 Kasanga 1 Samazi 152011001 1 2519 15 Rukwa 2 2 Sumbawanga 11 Kasanga 4 Kisumba 152011004 1 4881 15 Rukwa 2 2 Sumbawanga 11 Kasanga 11 Kasanga 152011011 1 2114 15 Rukwa 2 2 Sumbawanga 21 Mkowe 3 Mbuza 152021003 1 2196 15 Rukwa 2 2 Sumbawanga 43 Matai 4 Kalalasi 152043004 1 1965 15 Rukwa 2 2 Sumbawanga 51 Sopa 2 Mtuntumbe 152051002 1 1979 15 Rukwa 2 2 Sumbawanga 61 Mwazye 1 Kilesha 152061001 1 1139 15 Rukwa 2 2 Sumbawanga 61 Mwazye 4 Mwazye 152061004 1 3361 15 Rukwa 2 2 Sumbawanga 71 Katazi 6 Ninga 152071006 1 4911 15 Rukwa 2 2 Sumbawanga 81 Mwimbi 4 Kalepula 152081004 1 3669 15 Rukwa 2 2 Sumbawanga 81 Mwimbi 12 Mwimbi 152081012 1 4262 15 Rukwa 2 2 Sumbawanga 91 Mambwekenya 4 Madibila 152091004 1 1979 106 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 15 Rukwa 2 2 Sumbawanga 121 Miangalula 2 Miangalula 152121002 1 2687 15 Rukwa 2 2 Sumbawanga 133 Laela 1 Kititi 152133001 1 1743 15 Rukwa 2 2 Sumbawanga 141 Lusaka 5 Lowe 152141005 1 2058 15 Rukwa 2 2 Sumbawanga 151 Kalambazite 4 Kilembo 152151004 1 2276 15 Rukwa 2 2 Sumbawanga 151 Kalambazite 5 Ikozi 152151005 1 4845 15 Rukwa 2 2 Sumbawanga 171 Kaengesa 1 Lula 152171001 1 3128 15 Rukwa 2 2 Sumbawanga 171 Kaengesa 3 Mkunda 152171003 1 3418 15 Rukwa 2 2 Sumbawanga 181 Sandulula 2 Msanda Muungano 152181002 1 3392 15 Rukwa 2 2 Sumbawanga 181 Sandulula 6 Malolwa 152181006 1 2600 15 Rukwa 2 2 Sumbawanga 191 Muze 3 Muze 152191003 1 5434 15 Rukwa 2 2 Sumbawanga 191 Muze 9 Mpete 152191009 1 1704 15 Rukwa 2 2 Sumbawanga 203 Mtowisa 6 Kifinga 152203006 1 1247 15 Rukwa 2 2 Sumbawanga 211 Milepa 3 Ilemba 152211003 1 5192 15 Rukwa 2 2 Sumbawanga 221 Kaoze 3 Kyanda igonda 152221003 1 2332 15 Rukwa 2 2 Sumbawanga 221 Kaoze 6 Kapenta 152221006 1 3071 15 Rukwa 3 3 Nkasi 13 Namanyere 1 Mkole 153013001 1 2048 15 Rukwa 3 3 Nkasi 13 Namanyere 4 Kanazi 153013004 1 1301 15 Rukwa 3 3 Nkasi 13 Namanyere 10 Nkomolo - Kasongo II 153013010 1 116 15 Rukwa 3 3 Nkasi 21 Mtenga 3 Mwai 153021003 1 2322 15 Rukwa 3 3 Nkasi 21 Mtenga 5 Mashete 153021005 1 3528 15 Rukwa 3 3 Nkasi 31 Mkwamba 2 Tambaruka 153031002 1 1542 15 Rukwa 3 3 Nkasi 43 Chala 1 Kasu 153043001 1 3609 15 Rukwa 3 3 Nkasi 43 Chala 4 Katani - Shuleni 153043004 1 2466 15 Rukwa 3 3 Nkasi 51 Kipande 1 Kantawa - Mnanilo 153051001 1 2926 15 Rukwa 3 3 Nkasi 51 Kipande 4 Nkundi - Nanzumi 153051004 1 3671 15 Rukwa 3 3 Nkasi 61 Isale 1 Mtapenda - Ituntu 153061001 1 853 15 Rukwa 3 3 Nkasi 61 Isale 4 Msilihofu 153061004 1 1759 15 Rukwa 3 3 Nkasi 71 Kate 1 China 153071001 1 2805 15 Rukwa 3 3 Nkasi 71 Kate 4 Chalatila 153071004 1 1425 15 Rukwa 3 3 Nkasi 71 Kate 7 Kate 153071007 1 3472 15 Rukwa 3 3 Nkasi 81 Sintali 2 Nkana - Pimbi 153081002 1 3438 15 Rukwa 3 3 Nkasi 91 Kala 2 Kilambo 153091002 1 1398 15 Rukwa 3 3 Nkasi 91 Kala 5 Tundu 153091005 1 985 15 Rukwa 3 3 Nkasi 101 Wampelembe 3 Ng'undwe 153101003 1 1110 15 Rukwa 3 3 Nkasi 101 Wampelembe 6 Lyapinda 153101006 1 1747 15 Rukwa 3 3 Nkasi 111 Ninde 3 Namansi 153111003 1 2296 15 Rukwa 3 3 Nkasi 123 Kirando 3 Itete 153123003 1 962 15 Rukwa 3 3 Nkasi 123 Kirando 6 Katongolo 153123006 1 1938 15 Rukwa 3 3 Nkasi 123 Kirando 9 Mkinga 153123009 1 7670 15 Rukwa 3 3 Nkasi 133 Kabwe 3 Kanchui 153133003 1 851 15 Rukwa 3 3 Nkasi 133 Kabwe 6 Korongwe 153133006 1 7765 107 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 15 Rukwa 3 3 Nkasi 133 Kabwe 8 Kazovu 153133008 1 6979 15 Rukwa 4 4 Sumbawanga 13 Malangali 1 Makazi mapya 154013001 1 1009 15 Rukwa 4 4 Sumbawanga 53 Old Sumbawanga 1 Mbalika 154053001 1 1388 15 Rukwa 4 4 Sumbawanga 71 Ntendo 1 fyengereza 154071001 1 1596 15 Rukwa 4 4 Sumbawanga 71 Ntendo 3 Ntendo 154071003 1 3665 15 Rukwa 4 4 Sumbawanga 71 Ntendo 4 Kanondo 154071004 1 1342 15 Rukwa 4 4 Sumbawanga 81 Senga 1 Muva 154081001 1 1317 15 Rukwa 4 4 Sumbawanga 81 Senga 2 Kankwale 154081002 1 2186 15 Rukwa 4 4 Sumbawanga 81 Senga 3 Wipanga 154081003 1 2058 15 Rukwa 4 4 Sumbawanga 81 Senga 4 Lusanzi 154081004 1 614 15 Rukwa 4 4 Sumbawanga 81 Senga 5 Mponda 154081005 1 1485 15 Rukwa 4 4 Sumbawanga 91 Mollo 1 Malonje 154091001 1 2098 15 Rukwa 4 4 Sumbawanga 91 Mollo 2 Isesa 154091002 1 2422 15 Rukwa 4 4 Sumbawanga 91 Mollo 3 Ilinji 154091003 1 2259 15 Rukwa 4 4 Sumbawanga 91 Mollo 4 Mawenzusi 154091004 1 4065 15 Rukwa 4 4 Sumbawanga 101 Pito 1 Malagano 154101001 1 3155 15 Rukwa 4 4 Sumbawanga 101 Pito 2 Pito 154101002 1 1658 15 Rukwa 4 4 Sumbawanga 101 Pito 3 Katumba 154101003 1 3488 15 Rukwa 4 4 Sumbawanga 101 Pito 4 Tamasenga 154101004 1 4083 15 Rukwa 4 4 Sumbawanga 111 Milanzi 1 Milanzi 154111001 1 3130 15 Rukwa 4 4 Sumbawanga 111 Milanzi 2 Mlanda 154111002 1 5205 15 Rukwa 4 4 Sumbawanga 111 Milanzi 3 Nambogo 154111003 1 1422 15 Rukwa 4 4 Sumbawanga 121 Matanga 1 matanga 154121001 1 2984 15 Rukwa 4 4 Sumbawanga 121 Matanga 2 Kisumba 154121002 1 2168 15 Rukwa 4 4 Sumbawanga 121 Matanga 3 Chelenganya 154121003 1 1659 15 Rukwa 4 4 Sumbawanga 131 Kasense 1 kasense 154131001 1 2287 15 Rukwa 4 4 Sumbawanga 131 Kasense 2 Chipu 154131002 1 2942 15 Rukwa 4 4 Sumbawanga 131 Kasense 3 Mtimbwa 154131003 1 3716 16 Kigoma 1 1 Kibondo 13 Kibondo Mjini 1 Nengo 161013001 1 2973 16 Kigoma 1 1 Kibondo 13 Kibondo Mjini 3 Kumwambu 161013003 1 5363 16 Kigoma 1 1 Kibondo 21 Misezero 4 Kumuhama - Misezero 161021004 1 3350 16 Kigoma 1 1 Kibondo 31 Bunyambo 2 Bunyambo 161031002 1 2927 16 Kigoma 1 1 Kibondo 41 Kitahana 2 Kiahana 161041002 1 4515 16 Kigoma 1 1 Kibondo 51 Busagara 1 Kigendeka 161051001 1 4596 16 Kigoma 1 1 Kibondo 51 Busagara 3 Nyaruyoba 161051003 1 4187 16 Kigoma 1 1 Kibondo 61 Rugongwe 1 Kigaga 161061001 1 7011 16 Kigoma 1 1 Kibondo 61 Rugongwe 4 Busunzu 161061004 1 4141 16 Kigoma 1 1 Kibondo 71 Murungu 1 Kumuhasha 161071001 1 2393 16 Kigoma 1 1 Kibondo 83 Kakonko 3 Mbizi 161083003 1 3045 16 Kigoma 1 1 Kibondo 83 Kakonko 6 Kabingo 161083006 1 4675 16 Kigoma 1 1 Kibondo 91 Rugenge 1 Kasongati 161091001 1 4817 108 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 16 Kigoma 1 1 Kibondo 101 Kasuga 2 Kasuga 161101002 1 3167 16 Kigoma 1 1 Kibondo 111 Muhange 1 Gwarama 161111001 1 5220 16 Kigoma 1 1 Kibondo 111 Muhange 3 Muhange 161111003 1 5603 16 Kigoma 1 1 Kibondo 131 Nyamtukuza 1 Nyamtukuza 161131001 1 2220 16 Kigoma 1 1 Kibondo 141 Kasanda 1 Kazilamihunda 161141001 1 3801 16 Kigoma 1 1 Kibondo 141 Kasanda 3 Kasanda II 161141003 1 1775 16 Kigoma 1 1 Kibondo 151 Gwanumpu 3 Ilabiro 161151003 1 2923 16 Kigoma 1 1 Kibondo 151 Gwanumpu 4 Bukirilo 161151004 1 6955 16 Kigoma 1 1 Kibondo 161 Mugunzu 3 Nyagwijima 161161003 1 4316 16 Kigoma 1 1 Kibondo 173 Mabamba 3 Nyange 161173003 1 2574 16 Kigoma 1 1 Kibondo 181 Kizazi 1 Nyabitaka 161181001 1 3932 16 Kigoma 1 1 Kibondo 191 Kumsenga 1 Kumsenga 161191001 1 4366 16 Kigoma 1 1 Kibondo 191 Kumsenga 3 Kibuye 161191003 1 4725 16 Kigoma 1 1 Kibondo 201 Itaba 2 Mukabuye 161201002 1 5839 16 Kigoma 2 2 Kasulu 11 Kitanga 1 Kitanga 162011001 1 9150 16 Kigoma 2 2 Kasulu 21 Heru Shingo 3 Kigadye 162021003 1 3631 16 Kigoma 2 2 Kasulu 41 Nyamidaho 1 Mvugwe 162041001 1 6495 16 Kigoma 2 2 Kasulu 61 Kitagata 1 Kitagata - Nyundo 162061001 1 5349 16 Kigoma 2 2 Kasulu 71 Nyakitonto 2 Nyakitonto - Mkesha 'A' 162071002 1 8205 16 Kigoma 2 2 Kasulu 81 Nyamnyusi 3 Kanazi - Ruzilampene 162081003 1 6185 16 Kigoma 2 2 Kasulu 101 Ruhita 3 Migunga 162101003 1 4407 16 Kigoma 2 2 Kasulu 111 Titye 1 Lalambe 162111001 1 2418 16 Kigoma 2 2 Kasulu 121 Kigondo 2 Kidyama - Nyarumanga 162121002 1 4347 16 Kigoma 2 2 Kasulu 141 Rungwe Mpya 1 Rungwe Mpya - Manyovu 162141001 1 7226 16 Kigoma 2 2 Kasulu 141 Rungwe Mpya 2 Nyumbigwa 162141002 1 8180 16 Kigoma 2 2 Kasulu 151 Muzye 3 Muzye 162151003 1 4515 16 Kigoma 2 2 Kasulu 151 Muzye 6 Kasangezi 162151006 1 6370 16 Kigoma 2 2 Kasulu 161 Rusesa 2 Zeze - Msongeni 162161002 1 4154 16 Kigoma 2 2 Kasulu 181 Munzeze 1 Munzeze 162181001 1 8168 16 Kigoma 2 2 Kasulu 191 Muhunga 2 Heru Juu - Chogo 162191002 1 6292 16 Kigoma 2 2 Kasulu 201 Janda 1 Bukuba - Kibuye 162201001 1 7028 16 Kigoma 2 2 Kasulu 211 Rusaba 1 Kinazi - Mkoza 'A' 162211001 1 5996 16 Kigoma 2 2 Kasulu 221 Muhinda 1 Mwayaya 162221001 1 7946 16 Kigoma 2 2 Kasulu 221 Muhinda 2 Mihinda - Mbweru 'B' 162221002 1 5209 16 Kigoma 2 2 Kasulu 231 Munanila 2 Kibwigwa 162231002 1 6972 16 Kigoma 2 2 Kasulu 231 Munanila 4 Nyakimue 162231004 1 5571 16 Kigoma 2 2 Kasulu 241 Buhigwe 2 Mulera - Rulalo 162241002 1 3324 16 Kigoma 2 2 Kasulu 251 Nyamugali 2 Bulimanyi - Bweru 162251002 1 3240 16 Kigoma 2 2 Kasulu 261 Munyegera 2 Mwanga - Kibimba 162261002 1 4904 16 Kigoma 2 2 Kasulu 271 Kajana 2 Kajana - Rubuga 162271002 1 5078 109 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 16 Kigoma 2 2 Kasulu 291 Kilelema 1 Kilelema - Kidyama 162291001 1 5307 16 Kigoma 3 3 Kigoma R 11 Mkigo 2 Nyarubanda 163011002 1 6992 16 Kigoma 3 3 Kigoma R 21 Kalinzi 2 Matyazo 163021002 1 5434 16 Kigoma 3 3 Kigoma R 31 Bitale 2 Nyamhoza 163031002 1 4886 16 Kigoma 3 3 Kigoma R 31 Bitale 4 Bitale 163031004 1 6844 16 Kigoma 3 3 Kigoma R 41 Mahembe 1 Nkugwe 163041001 1 8091 16 Kigoma 3 3 Kigoma R 41 Mahembe 3 Chankabwimba 163041003 1 3876 16 Kigoma 3 3 Kigoma R 51 Matendo 2 Pamila 163051002 1 3498 16 Kigoma 3 3 Kigoma R 63 Uvinza 1 Basanza 163063001 1 8301 16 Kigoma 3 3 Kigoma R 71 Mtego wa Noti 1 Mtego wa Noti 163071001 1 4940 16 Kigoma 3 3 Kigoma R 83 Nguruka 2 Nyangabo 163083002 1 2919 16 Kigoma 3 3 Kigoma R 83 Nguruka 4 Itebula 163083004 1 3590 16 Kigoma 3 3 Kigoma R 91 Mganza 3 Malagarasi 163091003 1 6572 16 Kigoma 3 3 Kigoma R 111 Buhingu 1 Kalilani 163111001 1 2498 16 Kigoma 3 3 Kigoma R 121 Igalula 2 Igalula 163121002 1 5273 16 Kigoma 3 3 Kigoma R 131 Sigunga 1 Kaparamsenga 163131001 1 3541 16 Kigoma 3 3 Kigoma R 141 Sunuka 2 Kirando 163141002 1 5973 16 Kigoma 3 3 Kigoma R 141 Sunuka 5 Songambele 163141005 1 2562 16 Kigoma 3 3 Kigoma R 151 Ilagala 2 Mwakizega 163151002 1 10672 16 Kigoma 3 3 Kigoma R 161 Kandaga 1 Nyanganga 163161001 1 4490 16 Kigoma 3 3 Kigoma R 161 Kandaga 3 Kalenge 163161003 1 6209 16 Kigoma 3 3 Kigoma R 161 Kandaga 6 Kazuramimba 163161006 1 5733 16 Kigoma 3 3 Kigoma R 171 Simbo 1 Kaseke 163171001 1 8082 16 Kigoma 3 3 Kigoma R 181 Mngonya 1 Kamara 163181001 1 4178 16 Kigoma 3 3 Kigoma R 193 Mwandiga 2 Kibingo 163193002 1 4446 16 Kigoma 3 3 Kigoma R 201 Kagongo 2 Kagongo 163201002 1 3365 16 Kigoma 3 3 Kigoma R 211 Mwamgongo 1 Kiziba 163211001 1 7641 16 Kigoma 3 3 Kigoma R 221 Kagunga 1 Kagunga 163221001 1 7830 16 Kigoma 4 4 Kigoma U 13 Gungu 1 Bushabani 164013001 1 370 16 Kigoma 4 4 Kigoma U 23 Buhanda Businde 1 Buhanda 164023001 1 2039 16 Kigoma 4 4 Kigoma U 23 Buhanda Businde 2 Businde 164023002 1 1796 16 Kigoma 4 4 Kigoma U 33 Kagera 1 Kagera 164033001 1 1921 16 Kigoma 4 4 Kigoma U 123 Kigoma Bangwe 1 Kamala 164123001 1 6452 17 Shinyanga 1 1 Bariadi 11 Sapiwi 2 Masewa 171011002 1 6548 17 Shinyanga 1 1 Bariadi 11 Sapiwi 6 Sapiwi 171011006 1 6055 17 Shinyanga 1 1 Bariadi 23 Dutwa 5 Igaganulwa 171023005 1 4348 17 Shinyanga 1 1 Bariadi 31 Mwaubingi 3 Gasuma 171031003 1 6329 17 Shinyanga 1 1 Bariadi 41 Mwadobana 2 Banemhi 171041002 1 5663 17 Shinyanga 1 1 Bariadi 53 Nyakabindi 3 Old Maswa 171053003 1 3911 17 Shinyanga 1 1 Bariadi 63 Somanda 4 Matale 171063004 1 3912 17 Shinyanga 1 1 Bariadi 73 Nkololo 3 Mwasinasi 171073003 1 6813 110 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 17 Shinyanga 1 1 Bariadi 73 Nkololo 8 Nkololo 171073008 1 5676 17 Shinyanga 1 1 Bariadi 91 Sagata 3 Laini 171091003 1 5829 17 Shinyanga 1 1 Bariadi 101 Mwaswale 3 Nkuyu 171101003 1 6097 17 Shinyanga 1 1 Bariadi 111 Chinamili 3 Nanga 171111003 1 7468 17 Shinyanga 1 1 Bariadi 121 Mhunze 2 Shishani 171121002 1 6319 17 Shinyanga 1 1 Bariadi 131 Lagangabilili 1 Mitobo 171131001 1 3066 17 Shinyanga 1 1 Bariadi 131 Lagangabilili 5 Budalabujiga 171131005 1 5665 17 Shinyanga 1 1 Bariadi 141 Bunamhala 3 Bunamhala 171141003 1 6797 17 Shinyanga 1 1 Bariadi 151 Nkoma 2 Dasina 171151002 1 7538 17 Shinyanga 1 1 Bariadi 151 Nkoma 4 Nkoma 171151004 1 6231 17 Shinyanga 1 1 Bariadi 163 Mwamapalala 7 Ngeme 171163007 1 4459 17 Shinyanga 1 1 Bariadi 171 Zagayu 4 Kabale 171171004 1 2118 17 Shinyanga 1 1 Bariadi 191 Mbita 2 Sunzula 171191002 1 6266 17 Shinyanga 1 1 Bariadi 203 Lugulu 1 Ikungulipu 171203001 1 5778 17 Shinyanga 1 1 Bariadi 203 Lugulu 6 Nhobola 171203006 1 6177 17 Shinyanga 1 1 Bariadi 213 Bariadi 5 Bariadi 171213005 1 3732 17 Shinyanga 1 1 Bariadi 221 Sakwe 3 Itumbukilo 171221003 1 7877 17 Shinyanga 1 1 Bariadi 233 Mhango 3 Ngulyati 171233003 1 5160 17 Shinyanga 1 1 Bariadi 241 Kasoli 3 Mwamlapa 171241003 1 4423 17 Shinyanga 2 2 Maswa 11 Buchambi 1 Dodoma 172011001 1 4409 17 Shinyanga 2 2 Maswa 11 Buchambi 5 Sayusayu 172011005 1 4249 17 Shinyanga 2 2 Maswa 21 Isanga 1 Kidema 172021001 1 3438 17 Shinyanga 2 2 Maswa 21 Isanga 5 Njiapanda 172021005 1 2306 17 Shinyanga 2 2 Maswa 31 Masela 1 Seng'wa 172031001 1 5566 17 Shinyanga 2 2 Maswa 31 Masela 4 Mwasayi 172031004 1 4185 17 Shinyanga 2 2 Maswa 31 Masela 5 Masela 172031005 1 3623 17 Shinyanga 2 2 Maswa 43 Nyalikungu 3 Iyogelo 172043003 1 2760 17 Shinyanga 2 2 Maswa 53 Lalago 4 Lalago 172053004 1 3083 17 Shinyanga 2 2 Maswa 63 Dakama 1 Sangamwalugesha 172063001 1 3035 17 Shinyanga 2 2 Maswa 71 Sukuma 1 Mwabayanda 172071001 1 3754 17 Shinyanga 2 2 Maswa 71 Sukuma 4 Isagenghe 172071004 1 3701 17 Shinyanga 2 2 Maswa 81 Mpindo 1 Senani 172081001 1 6540 17 Shinyanga 2 2 Maswa 81 Mpindo 4 Zebeya 172081004 1 4434 17 Shinyanga 2 2 Maswa 91 Budekwa 2 Mwabalatulu 172091002 1 2482 17 Shinyanga 2 2 Maswa 91 Budekwa 4 Kiloleli 172091004 1 3368 17 Shinyanga 2 2 Maswa 101 Ipililo 2 Ikungulyankoma 172101002 1 3208 17 Shinyanga 2 2 Maswa 113 Malampaka 1 Nyabubinza 172113001 1 2325 17 Shinyanga 2 2 Maswa 113 Malampaka 4 Bukigi 172113004 1 3258 17 Shinyanga 2 2 Maswa 121 Badi 2 Muhida 172121002 1 6461 17 Shinyanga 2 2 Maswa 131 kulimi 2 Ilamata 172131002 1 2644 17 Shinyanga 2 2 Maswa 141 Nyabubinza 2 Mwabuumbu 172141002 1 4367 111 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 17 Shinyanga 2 2 Maswa 151 Shishiyu 1 Jija 172151001 1 6705 17 Shinyanga 2 2 Maswa 151 Shishiyu 2 Shishiyu 172151002 1 7851 17 Shinyanga 2 2 Maswa 161 Busilili 2 Masanwa 172161002 1 4127 17 Shinyanga 2 2 Maswa 171 Kadoto 1 Kadoto 172171001 1 5307 17 Shinyanga 2 2 Maswa 181 Nguliguli 1 Nguliguli 172181001 1 6963 17 Shinyanga 3 3 Shinyanga 11 Imesela 1 Mwamanyuda 173011001 1 3741 17 Shinyanga 3 3 Shinyanga 21 Usule 1 Sumbigu 173021001 1 2613 17 Shinyanga 3 3 Shinyanga 21 Usule 7 Bukene 173021007 1 1732 17 Shinyanga 3 3 Shinyanga 31 Ilola 2 Ihalo 173031002 1 4115 17 Shinyanga 3 3 Shinyanga 41 Didia 5 Nyambishi 173041005 1 2258 17 Shinyanga 3 3 Shinyanga 51 Itwangi 1 Zobogo 173051001 1 2017 17 Shinyanga 3 3 Shinyanga 63 Tinde 2 Jomu 173063002 1 4004 17 Shinyanga 3 3 Shinyanga 63 Tinde 5 Welezo 173063005 1 1214 17 Shinyanga 3 3 Shinyanga 63 Tinde 8 Nnumbili 173063008 1 2264 17 Shinyanga 3 3 Shinyanga 71 Mwakitolyo 5 Nyang'ombe 173071005 1 1438 17 Shinyanga 3 3 Shinyanga 83 Salawe 3 Songambele 173083003 1 5532 17 Shinyanga 3 3 Shinyanga 83 Salawe 5 Azimio 173083005 1 3024 17 Shinyanga 3 3 Shinyanga 91 Solwa 1 Mwakatola 173091001 1 1519 17 Shinyanga 3 3 Shinyanga 91 Solwa 5 Solwa 173091005 1 2681 17 Shinyanga 3 3 Shinyanga 101 Iselemagazi 2 Mwashilugula 173101002 1 2079 17 Shinyanga 3 3 Shinyanga 101 Iselemagazi 9 Ng'homango 173101009 1 3962 17 Shinyanga 3 3 Shinyanga 111 Lyabukande 2 Ihugi 173111002 1 3278 17 Shinyanga 3 3 Shinyanga 111 Lyabukande 4 Kizungu 173111004 1 7101 17 Shinyanga 3 3 Shinyanga 111 Lyabukande 5 Lyabukande 173111005 1 7103 17 Shinyanga 3 3 Shinyanga 121 Mwantini 1 Ng'wang'osha 173121001 1 2333 17 Shinyanga 3 3 Shinyanga 121 Mwantini 4 Kilimawe 173121004 1 1748 17 Shinyanga 3 3 Shinyanga 121 Mwantini 6 Zumwe 173121006 1 3148 17 Shinyanga 3 3 Shinyanga 131 Pandagichiza 3 Sayu 173131003 1 2525 17 Shinyanga 3 3 Shinyanga 131 Pandagichiza 6 Ng'walukwa 173131006 1 3708 17 Shinyanga 3 3 Shinyanga 151 Samuye 4 Mwang'hatanga 173151004 1 2435 17 Shinyanga 3 3 Shinyanga 151 Samuye 8 Isela 173151008 1 1656 17 Shinyanga 3 3 Shinyanga 161 Usanda 2 Manyada 173161002 1 3275 17 Shinyanga 4 4 Kahama 13 Bugarama 7 Bugarama 174013007 1 4791 17 Shinyanga 4 4 Kahama 21 Runguya 7 Lunguya 174021007 1 3672 17 Shinyanga 4 4 Kahama 33 Segese 10 Shilela 174033010 1 3421 17 Shinyanga 4 4 Kahama 51 Bulige 1 Kashishi 174051001 1 3785 17 Shinyanga 4 4 Kahama 81 Jana 1 Jana 174081001 1 2873 17 Shinyanga 4 4 Kahama 93 Isaka 1 Itogwang'holo/Isaka Station 174093001 1 6648 17 Shinyanga 4 4 Kahama 93 Isaka 3 Mwakata 174093003 1 5891 17 Shinyanga 4 4 Kahama 113 Isagehe 6 Mondo 174113006 1 2618 112 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 17 Shinyanga 4 4 Kahama 113 Isagehe 10 Kagongwa 174113010 1 4021 17 Shinyanga 4 4 Kahama 131 Kilago 10 Wame 174131010 1 942 17 Shinyanga 4 4 Kahama 141 Chona 5 Nsalaba 174141005 1 1362 17 Shinyanga 4 4 Kahama 161 Kisuke 2 Mapamba 174161002 1 2692 17 Shinyanga 4 4 Kahama 161 Kisuke 7 Ngokolo 174161007 1 2858 17 Shinyanga 4 4 Kahama 171 Ukune 9 Igunda 174171009 1 2984 17 Shinyanga 4 4 Kahama 181 Uyogo 4 Buyogo 174181004 1 4975 17 Shinyanga 4 4 Kahama 201 Ulowa 2 Kangeme 174201002 1 4803 17 Shinyanga 4 4 Kahama 211 Bulungwa 1 Nyamkondo/Kinamihwa 174211001 1 3976 17 Shinyanga 4 4 Kahama 211 Bulungwa 6 Nyamkende/Nyalwelwe II 174211006 1 9007 17 Shinyanga 4 4 Kahama 221 Idahina 5 Mwabomba 174221005 1 5220 17 Shinyanga 4 4 Kahama 231 Igwamanoni 5 Kipangu 174231005 1 1780 17 Shinyanga 4 4 Kahama 241 Mpunze 2 Mpunze 174241002 1 4372 17 Shinyanga 4 4 Kahama 251 Kinamapula 3 Hongwa 174251003 1 2771 17 Shinyanga 4 4 Kahama 251 Kinamapula 7 Ilemve 174251007 1 1141 17 Shinyanga 4 4 Kahama 271 Ngongwa 7 Wendele 174271007 1 3846 17 Shinyanga 4 4 Kahama 281 Ntobo 2 Ntobo ' A' 174281002 1 1888 17 Shinyanga 4 4 Kahama 313 Mhongolo 1 Nyashimbi 174313001 1 1757 17 Shinyanga 4 4 Kahama 333 Nyihogo 2 Mhungula/Bukondamoyo 174333002 1 2209 17 Shinyanga 5 5 Bukombe 11 Bukandwe 1 Nyanhwiga 175011001 1 2556 17 Shinyanga 5 5 Bukombe 11 Bukandwe 5 Bukandwe 175011005 1 3436 17 Shinyanga 5 5 Bukombe 23 Masumbwe 5 Shenda 175023005 1 2405 17 Shinyanga 5 5 Bukombe 31 Iyogelo 2 Bufanka 175031002 1 2176 17 Shinyanga 5 5 Bukombe 31 Iyogelo 7 Nyamakunkwa 175031007 1 1814 17 Shinyanga 5 5 Bukombe 41 Iponya 4 Nsango 175041004 1 1610 17 Shinyanga 5 5 Bukombe 51 Bukombe 2 Bukombe 175051002 1 4756 17 Shinyanga 5 5 Bukombe 51 Bukombe 7 Lyambamgongo 175051007 1 3434 17 Shinyanga 5 5 Bukombe 63 Ushirombo 3 Katome 175063003 1 6679 17 Shinyanga 5 5 Bukombe 63 Ushirombo 6 Mwalo 175063006 1 1499 17 Shinyanga 5 5 Bukombe 63 Ushirombo 11 Buntubili 175063011 1 3189 17 Shinyanga 5 5 Bukombe 63 Ushirombo 17 Kakoyoyo 175063017 1 5416 17 Shinyanga 5 5 Bukombe 63 Ushirombo 19 Butinzya II 175063019 1 4149 17 Shinyanga 5 5 Bukombe 71 Runzewe 2 Ludeba 175071002 1 4776 17 Shinyanga 5 5 Bukombe 71 Runzewe 4 Ikuzi 175071004 1 4889 17 Shinyanga 5 5 Bukombe 71 Runzewe 8 Msonga II 175071008 1 1024 17 Shinyanga 5 5 Bukombe 81 Ikunguigazi 4 Kashalo 175081004 1 3249 17 Shinyanga 5 5 Bukombe 91 Ilolangulu 1 Isebya 175091001 1 2624 17 Shinyanga 5 5 Bukombe 91 Ilolangulu 6 Mubamba 175091006 1 3450 17 Shinyanga 5 5 Bukombe 91 Ilolangulu 10 Bugalagala 175091010 1 5168 17 Shinyanga 5 5 Bukombe 111 Ushirika 1 Ivumwa 175111001 1 1899 113 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 17 Shinyanga 5 5 Bukombe 111 Ushirika 8 Ushirika 175111008 1 2170 17 Shinyanga 5 5 Bukombe 121 Nyasato 3 Bulugala 175121003 1 2334 17 Shinyanga 5 5 Bukombe 133 Uyovu 3 Busonzo 175133003 1 2772 17 Shinyanga 5 5 Bukombe 133 Uyovu 7 Namonge 175133007 1 3809 17 Shinyanga 5 5 Bukombe 133 Uyovu 13 Kanembwa 175133013 1 4388 17 Shinyanga 5 5 Bukombe 143 Lugunga 3 Mpakali 175143003 1 1426 17 Shinyanga 6 6 Meatu 13 Mwanhuzi 2 Mwambegwa 176013002 1 4686 17 Shinyanga 6 6 Meatu 13 Mwanhuzi 3 Mwanyahina 176013003 1 3422 17 Shinyanga 6 6 Meatu 13 Mwanhuzi 6 Bomani 176013006 1 2484 17 Shinyanga 6 6 Meatu 31 Kimali 1 Sapa 176031001 1 1374 17 Shinyanga 6 6 Meatu 31 Kimali 4 Mwangudo 176031004 1 2231 17 Shinyanga 6 6 Meatu 41 Mwamishali 3 Bulyashi 176041003 1 3208 17 Shinyanga 6 6 Meatu 51 Itinje 2 Itinje 176051002 1 2290 17 Shinyanga 6 6 Meatu 51 Itinje 4 Isengwa 176051004 1 4476 17 Shinyanga 6 6 Meatu 61 Kisesa 3 Kisesa 176061003 1 5846 17 Shinyanga 6 6 Meatu 71 Mwandoya 1 Mwakisandu 176071001 1 5112 17 Shinyanga 6 6 Meatu 71 Mwandoya 4 Mwandoya 176071004 1 6120 17 Shinyanga 6 6 Meatu 71 Mwandoya 5 Mwakaluba 176071005 1 6568 17 Shinyanga 6 6 Meatu 81 Lingeka 3 Mwaburutago 176081003 1 3086 17 Shinyanga 6 6 Meatu 81 Lingeka 5 Mwamhongo 176081005 1 3979 17 Shinyanga 6 6 Meatu 91 Sakasaka 2 Longalanhiga 176091002 1 5463 17 Shinyanga 6 6 Meatu 91 Sakasaka 3 Ming'ongwa 176091003 1 5137 17 Shinyanga 6 6 Meatu 101 Imalaseko 1 Nata 176101001 1 2326 17 Shinyanga 6 6 Meatu 111 Mwabuzo 1 Mwabuzo / Mwanzugi 176111001 1 4257 17 Shinyanga 6 6 Meatu 121 Mwamalole 2 Mwamanimba 176121002 1 2578 17 Shinyanga 6 6 Meatu 131 Mwanjoro 1 Mbushi 176131001 1 2822 17 Shinyanga 6 6 Meatu 141 Mwabuma 1 Mwabuma 176141001 1 5255 17 Shinyanga 6 6 Meatu 141 Mwabuma 3 Mwakasumbi 176141003 1 3585 17 Shinyanga 6 6 Meatu 151 Mwabusalu 2 Mwabusalu 176151002 1 7230 17 Shinyanga 6 6 Meatu 161 Lubiga 2 Lubiga 176161002 1 5682 17 Shinyanga 6 6 Meatu 171 Mwamanongu 1 Igushilu / Mwamagembe 176171001 1 2971 17 Shinyanga 6 6 Meatu 181 Ng'hoboko 2 Ng'hoboko 176181002 1 4282 17 Shinyanga 6 6 Meatu 191 Bukundi 1 Bukundi / Witamhiya 176191001 1 4537 17 Shinyanga 7 7 Shinyanga 11 Mwamalili 1 Bushora 177011001 1 1649 17 Shinyanga 7 7 Shinyanga 11 Mwamalili 2 Mwamalili 177011002 1 3238 17 Shinyanga 7 7 Shinyanga 11 Mwamalili 3 Seseko 177011003 1 1762 17 Shinyanga 7 7 Shinyanga 21 Kolandoto 1 Kolandoto 177021001 1 5305 17 Shinyanga 7 7 Shinyanga 21 Kolandoto 2 Mwamagunguli 177021002 1 2537 17 Shinyanga 7 7 Shinyanga 21 Kolandoto 3 Galamba 177021003 1 2839 17 Shinyanga 7 7 Shinyanga 33 Ngokolo 1 Ndembezi Shuleni 177033001 1 1350 17 Shinyanga 7 7 Shinyanga 41 Ibadakuli 1 Uzogole 177041001 1 3115 114 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 17 Shinyanga 7 7 Shinyanga 41 Ibadakuli 2 Ibadakuli 177041002 1 4530 17 Shinyanga 7 7 Shinyanga 41 Ibadakuli 3 Mwagala 177041003 1 2110 17 Shinyanga 7 7 Shinyanga 83 Kitangili 1 Imalilo 177083001 1 709 17 Shinyanga 7 7 Shinyanga 91 Kizumbi 1 Bugayambelele 177091001 1 3495 17 Shinyanga 7 7 Shinyanga 91 Kizumbi 2 Mwamashele 177091002 1 1820 17 Shinyanga 7 7 Shinyanga 91 Kizumbi 3 Nhelegani 177091003 1 3922 17 Shinyanga 7 7 Shinyanga 101 Mwawaza 1 Negezi 177101001 1 2165 17 Shinyanga 7 7 Shinyanga 101 Mwawaza 2 Mwawaza 177101002 1 2245 17 Shinyanga 7 7 Shinyanga 101 Mwawaza 3 Bugimbagu 177101003 1 1062 17 Shinyanga 7 7 Shinyanga 113 Ndala 1 Masekelo 177113001 1 1852 17 Shinyanga 7 7 Shinyanga 123 Kambarage 1 Mwasele A 177123001 1 908 17 Shinyanga 7 7 Shinyanga 123 Kambarage 2 Lubaga A 177123002 1 2962 17 Shinyanga 7 7 Shinyanga 133 Chibe 1 Ihapa 177133001 1 2893 17 Shinyanga 7 7 Shinyanga 133 Chibe 2 Butulwa 177133002 1 2393 17 Shinyanga 7 7 Shinyanga 133 Chibe 3 Chibe 177133003 1 5166 17 Shinyanga 8 8 Kishapu 11 Bunambiyu 2 Bunambiyu 178011002 1 2887 17 Shinyanga 8 8 Kishapu 21 Bubiki 1 Mwajiningu 178021001 1 1580 17 Shinyanga 8 8 Kishapu 21 Bubiki 4 Bubiki 178021004 1 3707 17 Shinyanga 8 8 Kishapu 21 Bubiki 6 Nyasamba 178021006 1 3253 17 Shinyanga 8 8 Kishapu 33 Songwa 4 Masagala 178033004 1 2616 17 Shinyanga 8 8 Kishapu 41 Seke/Bukoro 2 Seke - Ididi 178041002 1 2478 17 Shinyanga 8 8 Kishapu 51 Mondo 1 Wishiteleja 178051001 1 2835 17 Shinyanga 8 8 Kishapu 51 Mondo 4 Mwigumbi 178051004 1 3083 17 Shinyanga 8 8 Kishapu 63 Mwadui Lohumbo 2 Idukilo 178063002 1 5200 17 Shinyanga 8 8 Kishapu 63 Mwadui Lohumbo 3 Nyenze 178063003 1 3392 17 Shinyanga 8 8 Kishapu 71 Uchunga 1 Bupigi 178071001 1 3149 17 Shinyanga 8 8 Kishapu 71 Uchunga 5 Igaga 'A' 178071005 1 895 17 Shinyanga 8 8 Kishapu 83 Kishapu 1 Migunga 178083001 1 2117 17 Shinyanga 8 8 Kishapu 83 Kishapu 6 Isoso 178083006 1 1461 17 Shinyanga 8 8 Kishapu 91 Mwakipoya 2 Mwakipoya 178091002 1 2693 17 Shinyanga 8 8 Kishapu 101 Shagihilu 6 Sanjo 178101006 1 2025 17 Shinyanga 8 8 Kishapu 111 Somagedi 3 Kisesa 178111003 1 2367 17 Shinyanga 8 8 Kishapu 121 Mwamalasa 3 Kinampanda 178121003 1 3532 17 Shinyanga 8 8 Kishapu 131 Masanga 2 Mwang'halanga 178131002 1 2110 17 Shinyanga 8 8 Kishapu 131 Masanga 5 Mwakidalala 178131005 1 2346 17 Shinyanga 8 8 Kishapu 141 Lagana 4 Beledi 178141004 1 1412 17 Shinyanga 8 8 Kishapu 161 Ngofila 1 Inolelo 178161001 1 995 17 Shinyanga 8 8 Kishapu 171 Kiloleli 1 Kiloleli 178171001 1 2489 17 Shinyanga 8 8 Kishapu 181 Ukenyenge 1 Mayanji 178181001 1 1579 17 Shinyanga 8 8 Kishapu 191 Talaga 1 Kijongo 178191001 1 2645 17 Shinyanga 8 8 Kishapu 201 Itilima 1 Ipeja 178201001 1 1069 115 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 17 Shinyanga 8 8 Kishapu 201 Itilima 5 Ikoma 178201005 1 2296 18 Kagera 1 1 Karagwe 11 Kamuli 6 Kasoni 181011006 1 3165 18 Kagera 1 1 Karagwe 21 Mabira 2 Bugara 181021002 1 5508 18 Kagera 1 1 Karagwe 21 Mabira 6 Nyakashenyi 181021006 1 3295 18 Kagera 1 1 Karagwe 31 Igurwa 6 Kanoni 181031006 1 4500 18 Kagera 1 1 Karagwe 51 Kituntu 1 Kituntu 181051001 1 3780 18 Kagera 1 1 Karagwe 51 Kituntu 5 Kahundwe 181051005 1 1253 18 Kagera 1 1 Karagwe 71 Nkwenda 2 Muhurile 181071002 1 2664 18 Kagera 1 1 Karagwe 71 Nkwenda 5 Songambele 181071005 1 3707 18 Kagera 1 1 Karagwe 71 Nkwenda 8 Kitwechenkula I 181071008 1 7767 18 Kagera 1 1 Karagwe 81 Kimuli 2 Kikukuru 181081002 1 3601 18 Kagera 1 1 Karagwe 91 Ndama 1 Kagutu 181091001 1 1821 18 Kagera 1 1 Karagwe 103 Kayanga 1 Miti 181103001 1 1486 18 Kagera 1 1 Karagwe 121 Ihanda 1 Ihanda 181121001 1 4689 18 Kagera 1 1 Karagwe 133 Nyakahanga 1 Nyakahanga 181133001 1 5486 18 Kagera 1 1 Karagwe 141 Nyaishozi 2 Nyakayanja 181141002 1 3641 18 Kagera 1 1 Karagwe 181 Nyakakika 1 Kayungu 181181001 1 3745 18 Kagera 1 1 Karagwe 181 Nyakakika 3 Nyakakika 181181003 1 10625 18 Kagera 1 1 Karagwe 191 Bweranyange 2 Chamchuzi 181191002 1 7491 18 Kagera 1 1 Karagwe 201 Kibondo 3 Kakuraijo 181201003 1 2760 18 Kagera 1 1 Karagwe 221 Kiruruma 1 Kafunjo 181221001 1 4888 18 Kagera 1 1 Karagwe 221 Kiruruma 3 Kiruruma 181221003 1 5271 18 Kagera 1 1 Karagwe 231 Kyerwa 2 Nyaruzumbura 181231002 1 2800 18 Kagera 1 1 Karagwe 241 Isingiro 3 Karukwanzi 181241003 1 3903 18 Kagera 1 1 Karagwe 251 Kaisho 1 Rutunguru 181251001 1 3663 18 Kagera 1 1 Karagwe 271 Murongo 1 Rwabikagati 181271001 1 3094 18 Kagera 1 1 Karagwe 281 Bugomora 1 Nyamiyaga 181281001 1 5176 18 Kagera 1 1 Karagwe 281 Bugomora 4 Kigorogoro 181281004 1 5080 18 Kagera 2 2 Bukoba R 181 Rubafu 1 Rubafu 182181001 1 2516 18 Kagera 2 2 Bukoba R 191 Kishanje 3 Kishanje 182191003 1 2890 18 Kagera 2 2 Bukoba R 211 Buhendangabo 2 Rushaka 182211002 1 3126 18 Kagera 2 2 Bukoba R 221 Nyakato 4 Igombe 182221004 1 2098 18 Kagera 2 2 Bukoba R 231 Katoma 2 Kashenge 182231002 0 1888 18 Kagera 2 2 Bukoba R 241 Karabagaine 1 Kitwe 182241001 1 3249 18 Kagera 2 2 Bukoba R 251 Maruku 1 Kyansozi 182251001 1 1814 18 Kagera 2 2 Bukoba R 261 Kanyangereko 2 Butahyaibega 182261002 1 3546 18 Kagera 2 2 Bukoba R 271 Kyamuraile 1 Kyamuraile 182271001 1 4532 18 Kagera 2 2 Bukoba R 283 Katoro 2 Ngarama 182283002 1 3327 18 Kagera 2 2 Bukoba R 291 Kaibanja 2 Kaibanja 182291002 1 3440 18 Kagera 2 2 Bukoba R 301 Nyakibimbili 3 Kitahya 182301003 1 1866 18 Kagera 2 2 Bukoba R 311 Kasharu 2 Rutainamwa 182311002 1 2400 116 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 18 Kagera 2 2 Bukoba R 311 Kasharu 3 Ntoija 182311003 0 2026 18 Kagera 2 2 Bukoba R 321 Bujugo 1 Minazi 182321001 1 1764 18 Kagera 2 2 Bukoba R 333 Katerero 2 Kanazi 182333002 1 4021 18 Kagera 2 2 Bukoba R 333 Katerero 4 Mulahya 182333004 1 2541 18 Kagera 2 2 Bukoba R 351 Mikoni 1 Kagondo 182351001 1 2098 18 Kagera 2 2 Bukoba R 361 Ruhunga 1 Kobunshwi 182361001 1 3924 18 Kagera 2 2 Bukoba R 361 Ruhunga 3 Kihumuro 182361003 1 4859 18 Kagera 2 2 Bukoba R 371 Izimbya 2 Izimbya 182371002 1 4495 18 Kagera 2 2 Bukoba R 371 Izimbya 4 Kyaitoke 182371004 1 5673 18 Kagera 2 2 Bukoba R 381 Buterankuzi 3 Nyabushozi 182381003 1 1564 18 Kagera 2 2 Bukoba R 391 Rubale 1 Kabirizi 182391001 0 2516 18 Kagera 2 2 Bukoba R 391 Rubale 3 Rubale 182391003 1 3224 18 Kagera 2 2 Bukoba R 401 Kikomero 2 Kikomero 182401002 1 1778 18 Kagera 2 2 Bukoba R 411 Kibirizi 4 Bituntu 182411004 1 3256 18 Kagera 3 3 Muleba 11 Muhutwe 3 Kangantebe 183011003 1 2628 18 Kagera 3 3 Muleba 51 Izigo 2 Kabare 183051002 1 2507 18 Kagera 3 3 Muleba 51 Izigo 6 Bushumba 183051006 1 2390 18 Kagera 3 3 Muleba 61 Kagoma 5 Bigaga 183061005 1 1500 18 Kagera 3 3 Muleba 83 Muleba 1 Muleba Mjini 183083001 1 3476 18 Kagera 3 3 Muleba 91 Ikondo 3 Ikondo 183091003 1 2056 18 Kagera 3 3 Muleba 111 Magata/Karutanga 3 Katunguru 183111003 1 1847 18 Kagera 3 3 Muleba 121 Kibanga 1 Bumilo 183121001 1 1941 18 Kagera 3 3 Muleba 131 Kasharunga 6 Kiteme 183131006 1 3383 18 Kagera 3 3 Muleba 141 Kimwani 3 Katembe 183141003 1 2238 18 Kagera 3 3 Muleba 151 Kyebitembe 2 Kagasha 183151002 1 4339 18 Kagera 3 3 Muleba 161 Karambi 1 Kasharara 183161001 1 3652 18 Kagera 3 3 Muleba 171 Mubunda 1 Kishoju 183171001 1 3979 18 Kagera 3 3 Muleba 171 Mubunda 5 Bisheke 183171005 1 3110 18 Kagera 3 3 Muleba 181 Burungura 3 Kakoma 183181003 1 4594 18 Kagera 3 3 Muleba 191 Biirabo 3 Kabare 183191003 1 3346 18 Kagera 3 3 Muleba 201 Rushwa 2 Kyanshenge 183201002 1 3205 18 Kagera 3 3 Muleba 211 Ngenge 4 Kishuro 183211004 1 5028 18 Kagera 3 3 Muleba 221 kabirizi 2 Kihwera 183221002 1 2188 18 Kagera 3 3 Muleba 233 Nshamba 2 Rutenge 183233002 1 3043 18 Kagera 3 3 Muleba 241 Kashasha 1 Rubya 183241001 1 3194 18 Kagera 3 3 Muleba 251 Ijumbi 2 Rubao 183251002 1 1882 18 Kagera 3 3 Muleba 261 Kishanda 2 Ihunga 183261002 1 3722 18 Kagera 3 3 Muleba 271 Buganguzi 2 Kashozi 183271002 1 1870 18 Kagera 3 3 Muleba 281 Ibuga 2 Bunywambele 183281002 1 3365 18 Kagera 3 3 Muleba 291 Bulyakashaju 2 Rugando 183291002 1 2892 18 Kagera 3 3 Muleba 311 Ruhanga 2 Ruhanga 183311002 1 4132 117 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 18 Kagera 4 4 Biharamulo 13 B'mulo Mjini 1 Kiruruma 184013001 0 2683 18 Kagera 4 4 Biharamulo 13 B'mulo Mjini 3 Katelera 184013003 1 1940 18 Kagera 4 4 Biharamulo 13 B'mulo Mjini 4 Ruziba 184013004 0 2212 18 Kagera 4 4 Biharamulo 13 B'mulo Mjini 6 Nyarukongogo 184013006 0 2310 18 Kagera 4 4 Biharamulo 13 B'mulo Mjini 7 Nyakatuntu 184013007 1 2849 18 Kagera 4 4 Biharamulo 21 Nyarubungo 1 Kabukome 184021001 0 1655 18 Kagera 4 4 Biharamulo 21 Nyarubungo 2 Rusabya 184021002 1 3231 18 Kagera 4 4 Biharamulo 21 Nyarubungo 4 Nyamahanga 184021004 0 1792 18 Kagera 4 4 Biharamulo 21 Nyarubungo 6 Katoke 184021006 0 2753 18 Kagera 4 4 Biharamulo 21 Nyarubungo 7 Katahoka 184021007 1 4653 18 Kagera 4 4 Biharamulo 121 Nyamigogo 1 Kagoma 184121001 1 4457 18 Kagera 4 4 Biharamulo 121 Nyamigogo 2 Nyamigogo 184121002 0 4479 18 Kagera 4 4 Biharamulo 181 Nyabusozi 1 Isambala 184181001 0 2049 18 Kagera 4 4 Biharamulo 181 Nyabusozi 3 Mbindi 184181003 1 2898 18 Kagera 4 4 Biharamulo 181 Nyabusozi 4 Nemba 184181004 0 4919 18 Kagera 4 4 Biharamulo 191 Runazi 2 Kabindi 184191002 1 2782 18 Kagera 4 4 Biharamulo 191 Runazi 3 Rukora 184191003 0 1285 18 Kagera 4 4 Biharamulo 191 Runazi 5 Kikomakoma 184191005 1 4373 18 Kagera 4 4 Biharamulo 191 Runazi 6 Rwekubo 184191006 0 2832 18 Kagera 4 4 Biharamulo 201 Lusahunga 2 Nyakanazi 184201002 0 3245 18 Kagera 4 4 Biharamulo 201 Lusahunga 3 Nyantakala 184201003 1 5444 18 Kagera 4 4 Biharamulo 211 Kalenge 1 Kasato 184211001 1 3888 18 Kagera 4 4 Biharamulo 211 Kalenge 2 Ruganzu 184211002 0 2395 18 Kagera 4 4 Biharamulo 211 Kalenge 5 Nyamigere 184211005 0 4280 18 Kagera 4 4 Biharamulo 211 Kalenge 6 Kalenge 184211006 1 4114 18 Kagera 4 4 Biharamulo 221 Nyakahura 2 Mabare 184221002 0 3630 18 Kagera 4 4 Biharamulo 221 Nyakahura 4 Mihongoro 184221004 1 3203 18 Kagera 5 5 Ngara 11 Rusumo 1 Kasharazi 185011001 1 3193 18 Kagera 5 5 Ngara 11 Rusumo 3 Kasulo (I) 185011003 1 6135 18 Kagera 5 5 Ngara 21 Nyakisasa 1 Nyamahwa 185021001 1 6337 18 Kagera 5 5 Ngara 33 Rulenge 1 Kanyinya 185033001 1 2583 18 Kagera 5 5 Ngara 33 Rulenge 4 Muyenzi 185033004 1 2333 18 Kagera 5 5 Ngara 41 Keza 1 Kazingati 185041001 1 3366 18 Kagera 5 5 Ngara 51 Murusagamba 2 Ntanga 185051002 1 2376 18 Kagera 5 5 Ngara 51 Murusagamba 5 Murubanga 185051005 1 2666 18 Kagera 5 5 Ngara 61 Muganza 2 Mukalinzi 185061002 1 3771 18 Kagera 5 5 Ngara 71 Bugarama 2 Bugarama 185071002 1 2611 18 Kagera 5 5 Ngara 71 Bugarama 4 Nyarulama 185071004 1 3369 18 Kagera 5 5 Ngara 81 Bukiriro 2 Nyabihanga 185081002 1 2205 18 Kagera 5 5 Ngara 93 Kabanga 1 Murukukumbo 185093001 1 2919 18 Kagera 5 5 Ngara 93 Kabanga 3 Ngundus 185093003 1 2886 118 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 18 Kagera 5 5 Ngara 93 Kabanga 6 Ibuga 185093006 1 3169 18 Kagera 5 5 Ngara 101 Mabawe 2 Muhweza 185101002 1 2509 18 Kagera 5 5 Ngara 101 Mabawe 5 Mukalisa 185101005 1 1552 18 Kagera 5 5 Ngara 111 Kanazi 3 Mukalehe 185111003 1 2473 18 Kagera 5 5 Ngara 121 Mugoma 1 Mugoma 185121001 1 2171 18 Kagera 5 5 Ngara 121 Mugoma 4 Muruvyagira 185121004 1 2747 18 Kagera 5 5 Ngara 131 Kirushya 3 Kirushya 185131003 1 2113 18 Kagera 5 5 Ngara 141 Ntobeye 1 Ntobeye 185141001 1 4050 18 Kagera 5 5 Ngara 141 Ntobeye 3 Chivu 185141003 1 4955 18 Kagera 5 5 Ngara 151 Nyamiyaga 2 Murukulazo 185151002 1 4129 18 Kagera 5 5 Ngara 151 Nyamiyaga 4 Nyakiziba 185151004 1 6189 18 Kagera 5 5 Ngara 163 Ngara Mjini 3 Mukididiri 185163003 1 1969 18 Kagera 5 5 Ngara 171 Kibimba 2 Buhororo 185171002 1 2053 18 Kagera 6 6 Bukoba Urb 23 Nshambya 1 Kyaimyo & Ihyoro 186023001 1 1853 18 Kagera 6 6 Bukoba Urb 31 Buhembe 1 Kyashakati 186031001 1 3098 18 Kagera 6 6 Bukoba Urb 41 Kahororo 1 Bushwa 'A' & 'B' 186041001 1 2979 18 Kagera 6 6 Bukoba Urb 91 Ijuganyondo 1 Ibura 186091001 1 1912 18 Kagera 6 6 Bukoba Urb 101 Kitendaguro 1 Kanazi 186101001 1 3483 18 Kagera 6 6 Bukoba Urb 111 Kibeta 1 Igunga 186111001 1 3624 18 Kagera 6 6 Bukoba Urb 121 Kagondo 1 Kyakailabwa 186121001 1 1843 18 Kagera 6 6 Bukoba Urb 131 Nyanga 1 Ruchwera 186131001 1 1851 18 Kagera 2 7 Missenyi 13 Nsunga 2 Byamutemba 187013002 1 3240 18 Kagera 2 7 Missenyi 13 Nsunga 4 Ngando 187013004 0 4055 18 Kagera 2 7 Missenyi 21 Minziro 1 Kigazi 187021001 1 3439 18 Kagera 2 7 Missenyi 31 Kasambya 1 Mabuye 187031001 1 2314 18 Kagera 2 7 Missenyi 31 Kasambya 3 Gabulanga 187031003 1 2473 18 Kagera 2 7 Missenyi 31 Kasambya 4 Kasambya 187031004 0 2001 18 Kagera 2 7 Missenyi 31 Kasambya 6 Bunazi 187031006 1 5286 18 Kagera 2 7 Missenyi 43 Kyaka 4 Bulembo 187043004 1 1868 18 Kagera 2 7 Missenyi 43 Kyaka 5 Kashaba 187043005 0 2829 18 Kagera 2 7 Missenyi 51 Bugorora 3 Buchurago 187051003 1 2092 18 Kagera 2 7 Missenyi 61 Kilimilile 3 Kilimilile 187061003 1 3288 18 Kagera 2 7 Missenyi 61 Kilimilile 4 Mabale 187061004 0 2337 18 Kagera 2 7 Missenyi 71 Kakunyu 1 Kakunyu 187071001 1 2853 18 Kagera 2 7 Missenyi 71 Kakunyu 3 Bubale 187071003 1 5819 18 Kagera 2 7 Missenyi 91 Kashenye 1 Bukwali 187091001 0 2030 18 Kagera 2 7 Missenyi 101 Kanyigo 1 Kigarama 187101001 1 2206 18 Kagera 2 7 Missenyi 101 Kanyigo 5 Bushago 187101005 0 1262 18 Kagera 2 7 Missenyi 111 Ishunju 1 Kyelima 187111001 1 1551 18 Kagera 2 7 Missenyi 121 Ishozi 4 Katano 187121004 0 1028 18 Kagera 2 7 Missenyi 131 Gera 1 Kashekya 187131001 1 926 119 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 18 Kagera 2 7 Missenyi 141 Bwanjai 1 Bukabuye 187141001 0 1359 18 Kagera 2 7 Missenyi 141 Bwanjai 5 Rwamashonga 187141005 1 1225 18 Kagera 2 7 Missenyi 151 Bugandika 2 Igurugati 187151002 0 1315 18 Kagera 2 7 Missenyi 151 Bugandika 6 Bwemera 187151006 1 1007 18 Kagera 2 7 Missenyi 161 Kitobo 1 Kitobo 187161001 0 1491 18 Kagera 2 7 Missenyi 171 Buyango 1 Kikono 187171001 1 2455 18 Kagera 2 7 Missenyi 171 Buyango 2 Rutunga 187171002 0 1385 18 Kagera 4 8 Chato 31 Muganza 1 Nyabugera 188031001 1 5422 18 Kagera 4 8 Chato 31 Muganza 4 Bupandwampuli 188031004 0 2025 18 Kagera 4 8 Chato 31 Muganza 6 Katemwa Part I 188031006 1 8205 18 Kagera 4 8 Chato 41 Kigongo 2 Kikumbaitale 188041002 1 4697 18 Kagera 4 8 Chato 41 Kigongo 5 Kibehe 188041005 0 4054 18 Kagera 4 8 Chato 51 Nyamirembe 1 Kalebezo 188051001 1 3861 18 Kagera 4 8 Chato 61 Ichwankima 3 Ichwankima 188061003 0 824 18 Kagera 4 8 Chato 71 Ilemela 1 Ilemela 188071001 1 2255 18 Kagera 4 8 Chato 83 Chato 1 Mulumba 188083001 0 2096 18 Kagera 4 8 Chato 83 Chato 3 Mbuye 188083003 1 819 18 Kagera 4 8 Chato 83 Chato 7 Rubambangwe 188083007 0 2269 18 Kagera 4 8 Chato 91 Katende 2 Chabulongo 188091002 1 952 18 Kagera 4 8 Chato 101 Kachwamba 2 Igalula 188101002 1 2099 18 Kagera 4 8 Chato 101 Kachwamba 4 Mwangaza 188101004 0 1852 18 Kagera 4 8 Chato 111 Bukome 1 Nyabilezi 188111001 1 1369 18 Kagera 4 8 Chato 111 Bukome 5 Mkumbo 188111005 0 1827 18 Kagera 4 8 Chato 131 Makurugusi 1 Kibumba 188131001 1 6349 18 Kagera 4 8 Chato 131 Makurugusi 4 Musasa 188131004 1 3225 18 Kagera 4 8 Chato 143 Buseresere 2 Muranda 188143002 1 6135 18 Kagera 4 8 Chato 143 Buseresere 3 Buseresere 188143003 0 2899 18 Kagera 4 8 Chato 143 Buseresere 4 Butengo/Rumasa 188143004 1 6216 18 Kagera 4 8 Chato 143 Buseresere 7 Iparamasa 188143007 1 5581 18 Kagera 4 8 Chato 153 Bwanga 1 Minkoto 188153001 0 3144 18 Kagera 4 8 Chato 153 Bwanga 2 Kalembera 188153002 1 3964 18 Kagera 4 8 Chato 161 Bwera 1 Busaka 188161001 1 3114 18 Kagera 4 8 Chato 161 Bwera 3 Bwera 188161003 0 3087 18 Kagera 4 8 Chato 173 Buziku 1 Nyarutembo 188173001 1 3267 19 Mwanza 1 1 Ukerewe 71 Bukanda 1 Muhula 191071001 1 3979 19 Mwanza 1 1 Ukerewe 71 Bukanda 4 Hamuyebe 191071004 1 3386 19 Mwanza 1 1 Ukerewe 81 Mukituntu 2 Mahande 191081002 1 3724 19 Mwanza 1 1 Ukerewe 81 Mukituntu 5 Lutare 191081005 1 2675 19 Mwanza 1 1 Ukerewe 91 Igalla 2 Buhima 191091002 1 5012 19 Mwanza 1 1 Ukerewe 101 Bwiro 1 Serema 191101001 1 3027 19 Mwanza 1 1 Ukerewe 101 Bwiro 4 Busumba 191101004 1 4375 120 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 19 Mwanza 1 1 Ukerewe 113 Muriti 2 Igongo 191113002 1 2350 19 Mwanza 1 1 Ukerewe 113 Muriti 5 Bugula 191113005 1 6349 19 Mwanza 1 1 Ukerewe 121 Ilangala 2 Masonga 191121002 1 5242 19 Mwanza 1 1 Ukerewe 121 Ilangala 3 Murutilima 191121003 1 4579 19 Mwanza 1 1 Ukerewe 121 Ilangala 6 Kaseni 191121006 1 4078 19 Mwanza 1 1 Ukerewe 131 Namilembe 2 Nakamwa 191131002 1 2964 19 Mwanza 1 1 Ukerewe 131 Namilembe 5 Busagami 191131005 1 2164 19 Mwanza 1 1 Ukerewe 141 Nduruma 3 Chamuhunda 191141003 1 2462 19 Mwanza 1 1 Ukerewe 151 Murutunguru 1 Bugorola 191151001 1 5202 19 Mwanza 1 1 Ukerewe 151 Murutunguru 3 Murutunguru 191151003 1 4746 19 Mwanza 1 1 Ukerewe 161 Kagunguli 1 Buguza 191161001 1 4351 19 Mwanza 1 1 Ukerewe 161 Kagunguli 4 Buzegwe 191161004 1 4284 19 Mwanza 1 1 Ukerewe 171 Bukindo 1 Murutanga 191171001 1 2399 19 Mwanza 1 1 Ukerewe 171 Bukindo 4 Musozi 191171004 1 3229 19 Mwanza 1 1 Ukerewe 181 Namagondo 2 Namagondo 191181002 1 4434 19 Mwanza 1 1 Ukerewe 191 Ngoma 2 Nebuye 191191002 1 2825 19 Mwanza 1 1 Ukerewe 191 Ngoma 4 Muruseni 191191004 1 2931 19 Mwanza 1 1 Ukerewe 201 Bwisya 2 Nyang'ombe 191201002 1 3815 19 Mwanza 1 1 Ukerewe 221 Nyamanga 1 Chibasi 191221001 1 2452 19 Mwanza 1 1 Ukerewe 231 Bukiko 2 Bukiko 191231002 1 3300 19 Mwanza 2 2 Magu 13 Kisesa 2 Kitumba 192013002 1 4117 19 Mwanza 2 2 Magu 21 Bujashi 1 Matale 192021001 1 3119 19 Mwanza 2 2 Magu 31 Lutale 2 Itandula 192031002 1 2423 19 Mwanza 2 2 Magu 41 Kongolo 1 Kongolo 192041001 1 4476 19 Mwanza 2 2 Magu 61 Kitongo - Sima 3 Lugeye 192061003 1 4639 19 Mwanza 2 2 Magu 81 Kahangara 1 Nyamahanga 192081001 1 2274 19 Mwanza 2 2 Magu 81 Kahangara 6 Shinembo 192081006 1 2453 19 Mwanza 2 2 Magu 91 Nyigogo 5 Sagani 192091005 1 2928 19 Mwanza 2 2 Magu 111 Sukuma 1 Buhumbi 192111001 1 4692 19 Mwanza 2 2 Magu 111 Sukuma 4 Nyang'hanga 192111004 1 3343 19 Mwanza 2 2 Magu 121 Lubugu 4 Nsolla 192121004 1 2948 19 Mwanza 2 2 Magu 141 Mwamanyili 1 Mwamanyili 192141001 1 1830 19 Mwanza 2 2 Magu 141 Mwamanyili 4 Bulima 192141004 1 5465 19 Mwanza 2 2 Magu 161 Kabita 4 Nyamikoma 192161004 1 7060 19 Mwanza 2 2 Magu 173 Kalemela 1 Mayega 192173001 1 2366 19 Mwanza 2 2 Magu 173 Kalemela 4 Lamadi 192173004 1 4454 19 Mwanza 2 2 Magu 181 Mkula 2 Ng’wanihale 192181002 1 1662 19 Mwanza 2 2 Magu 181 Mkula 8 Kijilishi 192181008 1 5651 19 Mwanza 2 2 Magu 191 Igalukilo 4 Mwamagigisi 192191004 1 5286 19 Mwanza 2 2 Magu 201 Ngasamo 2 Ngasamo 192201002 1 2806 19 Mwanza 2 2 Magu 211 Malili 2 Gininiga 192211002 1 4213 121 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 19 Mwanza 2 2 Magu 211 Malili 4 Mwamigongwa 192211004 1 3340 19 Mwanza 2 2 Magu 231 Nyaluhande 3 Mwagindi 192231003 1 2090 19 Mwanza 2 2 Magu 241 Ng'haya 4 Bugatu 192241004 1 4258 19 Mwanza 2 2 Magu 251 Nkungulu 3 Kabila 192251003 1 4957 19 Mwanza 2 2 Magu 261 Shishani 1 Isolo 192261001 1 4202 19 Mwanza 2 2 Magu 261 Shishani 5 Nyasato 192261005 1 3052 19 Mwanza 4 4 Kwimba 11 Wala 2 Shilanona 194011002 1 3661 19 Mwanza 4 4 Kwimba 21 Bungulwa 1 Isunga 194021001 1 3043 19 Mwanza 4 4 Kwimba 31 Sumve 1 Sumve 194031001 1 4566 19 Mwanza 4 4 Kwimba 31 Sumve 4 Nyamikoma 194031004 1 1525 19 Mwanza 4 4 Kwimba 41 Mantare 3 Mwampulu 194041003 1 2729 19 Mwanza 4 4 Kwimba 51 Ngula 3 Nyambuyi 194051003 1 1905 19 Mwanza 4 4 Kwimba 71 Mwagi 3 Mwabilanda 194071003 1 2554 19 Mwanza 4 4 Kwimba 71 Mwagi 7 Ng'waging'hi 194071007 1 2312 19 Mwanza 4 4 Kwimba 81 Iseni 3 Icheja 194081003 1 1333 19 Mwanza 4 4 Kwimba 91 Nyambiti 4 Ibindo 194091004 1 3623 19 Mwanza 4 4 Kwimba 101 Maligisu 2 Kadashi 194101002 1 4447 19 Mwanza 4 4 Kwimba 101 Maligisu 4 Maligisu 194101004 1 4565 19 Mwanza 4 4 Kwimba 123 Malya 1 Mwitambu 194123001 1 2343 19 Mwanza 4 4 Kwimba 131 Lyoma 2 Lyoma 194131002 1 2504 19 Mwanza 4 4 Kwimba 141 Mwang'halanga 2 Mahiga 194141002 1 2705 19 Mwanza 4 4 Kwimba 161 Mwakilyambiti 1 Mwakilyambiti 194161001 1 3042 19 Mwanza 4 4 Kwimba 161 Mwakilyambiti 4 Mwamakoye 194161004 1 4478 19 Mwanza 4 4 Kwimba 171 Hungumalwa 3 Hungumalwa 194171003 1 4981 19 Mwanza 4 4 Kwimba 171 Hungumalwa 5 Manai 194171005 1 3399 19 Mwanza 4 4 Kwimba 181 Mwamala 2 Kijida 194181002 1 2867 19 Mwanza 4 4 Kwimba 191 Kikubiji 1 Mwalubungwe 194191001 1 1967 19 Mwanza 4 4 Kwimba 191 Kikubiji 4 Mwabayanda 194191004 1 2798 19 Mwanza 4 4 Kwimba 201 Mhande 4 Izizimba 'A' 194201004 1 3470 19 Mwanza 4 4 Kwimba 211 Bupamwa 2 Chasalawi 194211002 1 3173 19 Mwanza 4 4 Kwimba 231 Ng'hundi 1 Jojiro 194231001 1 3151 19 Mwanza 4 4 Kwimba 241 Igongwa 2 Manguluma 194241002 1 3130 19 Mwanza 4 4 Kwimba 253 Ngudu 1 Welamasonga 194253001 1 4057 19 Mwanza 5 5 Sengerema 21 Nyamazugo 3 Nyamizeze 195021003 1 3702 19 Mwanza 5 5 Sengerema 31 Chifunfu 3 Nyakahako 195031003 1 4658 19 Mwanza 5 5 Sengerema 31 Chifunfu 6 Kasenyi 195031006 1 4140 19 Mwanza 5 5 Sengerema 41 Katunguru 5 Katunguru 195041005 1 5584 19 Mwanza 5 5 Sengerema 51 Kasungamile 3 Kasungamile 195051003 1 2264 19 Mwanza 5 5 Sengerema 61 Nyamatongo 2 Karumo 195061002 1 3575 19 Mwanza 5 5 Sengerema 61 Nyamatongo 6 Ngoma 'B' 195061006 1 2597 19 Mwanza 5 5 Sengerema 71 Tabaruka 5 Nyampande 195071005 1 3245 122 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 19 Mwanza 5 5 Sengerema 81 Busisi 2 Kahumulo 195081002 1 2982 19 Mwanza 5 5 Sengerema 101 Igalula 1 Ngoma 'A' 195101001 1 5507 19 Mwanza 5 5 Sengerema 111 Kagunga 4 Nyanchenhe 195111004 1 4858 19 Mwanza 5 5 Sengerema 121 Sima 4 Ijinga 195121004 1 1799 19 Mwanza 5 5 Sengerema 131 Nyakasungwa 2 Kasisa 195131002 1 7471 19 Mwanza 5 5 Sengerema 141 Kalebezo 2 Nyashana 195141002 1 1696 19 Mwanza 5 5 Sengerema 151 Nyehunge 2 Nyamadoke 195151002 1 3640 19 Mwanza 5 5 Sengerema 151 Nyehunge 4 Nyehunge I 195151004 1 9128 19 Mwanza 5 5 Sengerema 171 Bupandwamhela 1 Iligamba 195171001 1 6595 19 Mwanza 5 5 Sengerema 171 Bupandwamhela 4 Bupandwamhela I 195171004 1 8533 19 Mwanza 5 5 Sengerema 181 Katwe 5 Kasheka 195181005 1 2357 19 Mwanza 5 5 Sengerema 201 Kazunzu 1 Lushamba 195201001 1 9653 19 Mwanza 5 5 Sengerema 201 Kazunzu 3 Itabagumba 195201003 1 7127 19 Mwanza 5 5 Sengerema 201 Kazunzu 9 Luharanyonga 195201009 1 3121 19 Mwanza 5 5 Sengerema 211 Lugata 4 Lugata I 195211004 1 10047 19 Mwanza 5 5 Sengerema 221 Nyakalilo 2 Nyakalilo 195221002 1 7944 19 Mwanza 5 5 Sengerema 221 Nyakalilo 4 Sukuma 195221004 1 4633 19 Mwanza 5 5 Sengerema 231 Nyakasasa 4 Isenyi 195231004 1 3542 19 Mwanza 5 5 Sengerema 251 Nyanzenda 1 Luchili 195251001 1 6277 19 Mwanza 6 6 Geita 11 Nzera 4 Lwezera 196011004 1 10796 19 Mwanza 6 6 Geita 23 Nkome 2 Katoma 196023002 1 6541 19 Mwanza 6 6 Geita 31 Kagu 5 Bugulula 196031005 1 5774 19 Mwanza 6 6 Geita 41 Senga 3 Senga 196041003 1 7419 19 Mwanza 6 6 Geita 53 Katoro 2 Katoro 196053002 1 6588 19 Mwanza 6 6 Geita 53 Katoro 7 Ibondo 196053007 1 4918 19 Mwanza 6 6 Geita 71 Nyachiluluma 5 Kasang'wa 196071005 1 6111 19 Mwanza 6 6 Geita 91 Bukwimba 1 Bulangale 196091001 1 1909 19 Mwanza 6 6 Geita 121 Busanda 2 Msasa 196121002 1 5004 19 Mwanza 6 6 Geita 131 Bukoli 3 Ihega 196131003 1 2542 19 Mwanza 6 6 Geita 141 Nyamalimbe 4 Buzanaki 196141004 1 4037 19 Mwanza 6 6 Geita 151 Nyakamwaga 2 Nyakamwaga 196151002 1 2658 19 Mwanza 6 6 Geita 161 Kamena 5 Nyalwanzaja 196161005 1 4872 19 Mwanza 6 6 Geita 171 Nyang'hwale 3 Ibambila 196171003 1 2203 19 Mwanza 6 6 Geita 181 Busolwa 4 Busolwa 196181004 1 5730 19 Mwanza 6 6 Geita 191 Shabaka 3 Lubando 196191003 1 1395 19 Mwanza 6 6 Geita 213 Kalangalala 4 Nyankumbu 196213004 1 5512 19 Mwanza 6 6 Geita 221 Mtakuja 4 Nyakabale 196221004 1 1930 19 Mwanza 6 6 Geita 251 Bulela 1 Gamashi 196251001 1 1891 19 Mwanza 6 6 Geita 251 Bulela 5 Nyaseke 196251005 1 2996 19 Mwanza 6 6 Geita 261 Kamhanga 5 Misiri 196261005 1 2588 19 Mwanza 6 6 Geita 271 Lubanga 3 Lubanga 196271003 1 3672 123 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 19 Mwanza 6 6 Geita 271 Lubanga 7 Nyakaduha 196271007 1 3045 19 Mwanza 6 6 Geita 291 Nyakagomba 3 Nyakagomba 196291003 1 4295 19 Mwanza 6 6 Geita 311 Kafita 1 Lushimba 196311001 1 2603 19 Mwanza 6 6 Geita 331 Nyarugusu 2 Wigo 196331002 1 4180 19 Mwanza 6 6 Geita 331 Nyarugusu 5 Nyarugusu I 196331005 1 7395 19 Mwanza 7 7 Missungwi 11 Bulemeji 2 Buganda 197011002 1 2393 19 Mwanza 7 7 Missungwi 21 Idetemya 1 Bukumbi 197021001 1 4001 19 Mwanza 7 7 Missungwi 21 Idetemya 3 Isamilo 197021003 1 4458 19 Mwanza 7 7 Missungwi 33 Usagara 2 Nyang'homango 197033002 1 3224 19 Mwanza 7 7 Missungwi 41 Ukiriguru 1 Nyang'holongo 197041001 1 2169 19 Mwanza 7 7 Missungwi 51 Kanyelele 2 Gambajiga 197051002 1 2953 19 Mwanza 7 7 Missungwi 61 Koromije 2 Mwalwigi 197061002 1 2034 19 Mwanza 7 7 Missungwi 61 Koromije 6 Koromije 197061006 1 2748 19 Mwanza 7 7 Missungwi 71 Igokelo 2 Wanzamiso 197071002 1 1622 19 Mwanza 7 7 Missungwi 71 Igokelo 5 Mwajombo 197071005 1 4305 19 Mwanza 7 7 Missungwi 81 Mwaniko 2 Mondo 197081002 1 4354 19 Mwanza 7 7 Missungwi 93 Missungwi 1 Iteja 197093001 1 5063 19 Mwanza 7 7 Missungwi 93 Missungwi 3 Lubuga 197093003 1 4490 19 Mwanza 7 7 Missungwi 93 Missungwi 4 Mabuki I 197093004 1 7960 19 Mwanza 7 7 Missungwi 103 Misasi 3 Mwasagela 197103003 1 2565 19 Mwanza 7 7 Missungwi 111 Kijima 3 Mwamaguhwa 197111003 1 2573 19 Mwanza 7 7 Missungwi 121 Shilalo 3 Mwamboku 197121003 1 3584 19 Mwanza 7 7 Missungwi 131 Buhingo 2 Buhingo 197131002 1 2043 19 Mwanza 7 7 Missungwi 141 Busongo 2 Kifune 197141002 1 2285 19 Mwanza 7 7 Missungwi 151 Nhundulu 1 Mwagiligili 197151001 1 4386 19 Mwanza 7 7 Missungwi 151 Nhundulu 3 Ibinza 197151003 1 1689 19 Mwanza 7 7 Missungwi 161 Luburi 2 Ilalambogo 197161002 1 2229 19 Mwanza 7 7 Missungwi 171 Ilujamate 3 Mbalama 197171003 1 1808 19 Mwanza 7 7 Missungwi 181 Mbarika 2 Mbarika 197181002 1 2819 19 Mwanza 7 7 Missungwi 191 Sumbugu 1 Sumbugu 197191001 1 3542 19 Mwanza 7 7 Missungwi 191 Sumbugu 4 Kwimwa 197191004 1 2746 19 Mwanza 7 7 Missungwi 201 Kasololo 3 Igumo 197201003 1 4276 19 Mwanza 8 8 Ilemela 43 Igoma 1 Kishili 198043001 1 4168 19 Mwanza 8 8 Ilemela 43 Igoma 2 Fumagila 198043002 1 2204 19 Mwanza 8 8 Ilemela 51 Sangabuye 1 Kabusungu 198051001 1 2359 19 Mwanza 8 8 Ilemela 51 Sangabuye 2 Nyafula 198051002 1 3385 19 Mwanza 8 8 Ilemela 51 Sangabuye 3 Sangabuye 198051003 1 3191 19 Mwanza 8 8 Ilemela 61 Bugogwa 1 Igogwe 198061001 1 5349 19 Mwanza 8 8 Ilemela 61 Bugogwa 2 Nyamwilolelwa 198061002 1 7630 19 Mwanza 8 8 Ilemela 61 Bugogwa 3 Igombe 198061003 1 11303 19 Mwanza 8 8 Ilemela 73 Ilemela 1 Kiseke 198073001 1 4874 124 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 19 Mwanza 8 8 Ilemela 73 Ilemela 2 Kahama 198073002 1 3608 19 Mwanza 8 8 Ilemela 81 Mkolani 1 Luchelele 198081001 1 7722 19 Mwanza 8 8 Ilemela 81 Mkolani 2 Mkolani 198081002 1 7559 19 Mwanza 8 8 Ilemela 91 Buhongwa 1 Buhongwa 198091001 1 5375 19 Mwanza 8 8 Ilemela 91 Buhongwa 2 Lwanhima 198091002 1 4480 19 Mwanza 8 8 Ilemela 101 Buswelu 1 Buswelu 198101001 1 6029 19 Mwanza 8 8 Ilemela 101 Buswelu 2 Nyamadoke 198101002 1 1892 19 Mwanza 8 8 Ilemela 101 Buswelu 3 Nyamhongolo 198101003 1 3446 20 Mara 1 1 Tarime 21 Mwema 1 Kubiterere 201021001 1 3149 20 Mara 1 1 Tarime 21 Mwema 3 Nyamuhunda 201021003 1 2042 20 Mara 1 1 Tarime 33 Sirari 3 Ng'ereng'ere 201033003 0 2109 20 Mara 1 1 Tarime 41 Pemba 1 Nyabisaga 201041001 1 4153 20 Mara 1 1 Tarime 41 Pemba 6 Borega 'B' 201041006 1 2166 20 Mara 1 1 Tarime 51 Nyakonga 2 Ganyange 201051002 0 2798 20 Mara 1 1 Tarime 61 Nyarero 1 Soroneta 201061001 1 2647 20 Mara 1 1 Tarime 61 Nyarero 4 Nyarero 201061004 1 2756 20 Mara 1 1 Tarime 71 Nyamwaga 2 Keisangura 201071002 0 3484 20 Mara 1 1 Tarime 71 Nyamwaga 3 Nyamwaga 201071003 1 4372 20 Mara 1 1 Tarime 91 Nyanungu 1 Itiryo 201091001 1 5775 20 Mara 1 1 Tarime 91 Nyanungu 2 Mangucha 201091002 1 5250 20 Mara 1 1 Tarime 91 Nyanungu 4 Kangariani 201091004 0 3336 20 Mara 1 1 Tarime 101 Gorong'a 1 Masanga 201101001 1 4737 20 Mara 1 1 Tarime 111 Nyarokoba 1 Genkuru 201111001 1 5211 20 Mara 1 1 Tarime 121 Kemambo 1 Kewanja 201121001 1 3688 20 Mara 1 1 Tarime 131 Kibasuka 2 Nyarwana 201131002 1 3798 20 Mara 1 1 Tarime 131 Kibasuka 3 Nyakunguru 201131003 0 4116 20 Mara 1 1 Tarime 141 Binagi 2 Magoma 201141002 1 3154 20 Mara 1 1 Tarime 153 Turwa 1 Magena 201153001 1 2314 20 Mara 1 1 Tarime 153 Turwa 4 Tagota 201153004 1 3392 20 Mara 1 1 Tarime 171 Nyandoto 1 Kemange 201171001 0 3712 20 Mara 1 1 Tarime 171 Nyandoto 5 Gamasara 201171005 1 2166 20 Mara 1 1 Tarime 191 Manga 3 Bisarwi 201191003 1 2620 20 Mara 1 1 Tarime 191 Manga 4 Nyamerambaro 201191004 0 1482 20 Mara 1 1 Tarime 401 Bumera 4 Kwisarara 201401004 0 2310 20 Mara 1 1 Tarime 413 Matongo 1 Matongo 201413001 1 2941 20 Mara 2 2 Serengeti 11 Kenyamonta 3 Nyagasense 202011003 1 3753 20 Mara 2 2 Serengeti 21 Busawe 1 Gantamome 202021001 1 2800 20 Mara 2 2 Serengeti 21 Busawe 3 Nyamakobiti 202021003 1 2533 20 Mara 2 2 Serengeti 31 Kisaka 2 Nyiboko 202031002 1 2496 20 Mara 2 2 Serengeti 41 Kebanchabancha 1 Musati 202041001 1 2418 20 Mara 2 2 Serengeti 41 Kebanchabancha 3 Nyansurura 202041003 1 2831 125 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 20 Mara 2 2 Serengeti 51 Ring'wani 1 Kenyana 202051001 1 1865 20 Mara 2 2 Serengeti 51 Ring'wani 4 Remung'orori 202051004 1 1938 20 Mara 2 2 Serengeti 61 Rung'abure 1 Gesarya 202061001 1 3058 20 Mara 2 2 Serengeti 61 Rung'abure 3 Rung'abure 202061003 1 3004 20 Mara 2 2 Serengeti 71 Machochwe 3 Nyamakendo 202071003 1 3922 20 Mara 2 2 Serengeti 81 Kisangura 1 Nyamburi 202081001 1 2926 20 Mara 2 2 Serengeti 81 Kisangura 3 Kisangura 202081003 1 3132 20 Mara 2 2 Serengeti 93 Mugumu Mjini 1 Matare 202093001 1 1828 20 Mara 2 2 Serengeti 101 Ikoma 3 Robanda 202101003 1 1492 20 Mara 2 2 Serengeti 111 Natta 1 Kono 202111001 1 896 20 Mara 2 2 Serengeti 111 Natta 4 Makundusi 202111004 1 2227 20 Mara 2 2 Serengeti 131 Rigicha 2 Rigicha 202131002 1 1940 20 Mara 2 2 Serengeti 131 Rigicha 4 Kitembere 202131004 1 1831 20 Mara 2 2 Serengeti 141 Nyambureti 2 Mununa 202141002 1 1609 20 Mara 2 2 Serengeti 151 Nyamoko 1 Itununu 202151001 1 3224 20 Mara 2 2 Serengeti 151 Nyamoko 4 kwitete 202151004 1 1935 20 Mara 2 2 Serengeti 161 Manchira 2 Bonchugu 202161002 1 4453 20 Mara 2 2 Serengeti 161 Manchira 4 Misseke 202161004 1 2178 20 Mara 2 2 Serengeti 171 Kyambahi 2 Nyichoka 202171002 1 3243 20 Mara 2 2 Serengeti 181 Nyamatare 3 Mosongo 202181003 1 4755 20 Mara 2 2 Serengeti 181 Nyamatare 4 Nyamatoke 202181004 1 2385 20 Mara 3 3 Musoma R 11 Buswahili 3 Buswahili 203011003 1 1875 20 Mara 3 3 Musoma R 21 Nyamimange 3 Nyamimange 203021003 1 3008 20 Mara 3 3 Musoma R 31 Bwiregi 2 Ryamisanga 203031002 1 3664 20 Mara 3 3 Musoma R 41 Muriaza 3 Muriaza 203041003 1 3084 20 Mara 3 3 Musoma R 51 Buhemba 3 Magunga 203051003 1 3706 20 Mara 3 3 Musoma R 63 Butiama 1 Butiama 203063001 1 5578 20 Mara 3 3 Musoma R 71 Masaba 2 Nyasirori 203071002 1 3218 20 Mara 3 3 Musoma R 93 Kukirango 2 Nyamisisye 203093002 1 4959 20 Mara 3 3 Musoma R 101 Buruma 1 Isaba 203101001 1 3167 20 Mara 3 3 Musoma R 101 Buruma 4 Rwamugabo 203101004 1 2108 20 Mara 3 3 Musoma R 111 Butuguri 3 Kisamwene 203111003 1 3394 20 Mara 3 3 Musoma R 121 Bukabwa 3 Mmazami 203121003 1 3819 20 Mara 3 3 Musoma R 131 Nyankanga 2 Nyankanga 203131002 1 5138 20 Mara 3 3 Musoma R 131 Nyankanga 5 Nyabekwabi 203131005 1 4110 20 Mara 3 3 Musoma R 141 Etaro 3 Nyegina 203141003 1 4449 20 Mara 3 3 Musoma R 151 Nyakatende 2 Kiemba 203151002 1 3211 20 Mara 3 3 Musoma R 171 Kiriba 1 Kiriba 203171001 1 2781 20 Mara 3 3 Musoma R 171 Kiriba 4 Bwai - Kumsoma 203171004 1 5043 20 Mara 3 3 Musoma R 181 Tegeruka 3 Mayani 203181003 1 2416 20 Mara 3 3 Musoma R 191 Suguti 4 Wanyere 203191004 1 2961 126 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 20 Mara 3 3 Musoma R 201 Nyambono 3 Bugoji 203201003 1 4161 20 Mara 3 3 Musoma R 211 Nyamrandirira 2 Kasoma 203211002 1 3886 20 Mara 3 3 Musoma R 231 Murangi 1 Lyasembe 203231001 1 2710 20 Mara 3 3 Musoma R 241 Bukima 1 Bukima 203241001 1 3574 20 Mara 3 3 Musoma R 241 Bukima 4 Rusoli 203241004 1 2840 20 Mara 3 3 Musoma R 261 Bwasi 4 Bwasi 203261004 1 2526 20 Mara 3 3 Musoma R 271 Bukumi 3 Busekera 203271003 1 4223 20 Mara 4 4 Bunda 13 Nyamuswa 2 sarawe 204013002 1 1956 20 Mara 4 4 Bunda 21 Salama 1 Nyaburundu 204021001 1 2420 20 Mara 4 4 Bunda 21 Salama 4 Salama 'A' 204021004 1 2597 20 Mara 4 4 Bunda 31 Mihingo 1 Mikoramiro 204031001 1 3299 20 Mara 4 4 Bunda 31 Mihingo 3 Mihingo 204031003 1 2412 20 Mara 4 4 Bunda 41 Mugeta 2 Nyamg'aranga 204041002 1 3038 20 Mara 4 4 Bunda 41 Mugeta 4 Mugeta 204041004 1 2017 20 Mara 4 4 Bunda 51 Hunyari 3 Hunyari 204051003 1 4065 20 Mara 4 4 Bunda 61 Mcharo 2 Changuge 204061002 1 1788 20 Mara 4 4 Bunda 71 Sazira 1 Kitaramaka 204071001 1 1800 20 Mara 4 4 Bunda 71 Sazira 4 Ligamba'B' 204071004 1 1570 20 Mara 4 4 Bunda 81 Kunzugu 3 Tamau 204081003 1 1552 20 Mara 4 4 Bunda 101 Guta 1 Kinyambwiga 204101001 1 3290 20 Mara 4 4 Bunda 101 Guta 3 Guta 204101003 1 4608 20 Mara 4 4 Bunda 111 Butimba 2 Buzimbwe 204111002 1 2130 20 Mara 4 4 Bunda 111 Butimba 6 Ragata 204111006 1 1916 20 Mara 4 4 Bunda 121 Neruma 3 Mahyoro 204121003 1 1907 20 Mara 4 4 Bunda 133 Kibara 1 Nakatuba 204133001 1 1879 20 Mara 4 4 Bunda 141 Nansimo 1 Nambaza 204141001 1 2077 20 Mara 4 4 Bunda 141 Nansimo 4 Nafuba 204141004 1 2385 20 Mara 4 4 Bunda 151 Kisorya 3 Kisorya 204151003 1 3394 20 Mara 4 4 Bunda 161 Igundu 1 Igundu 204161001 1 3246 20 Mara 4 4 Bunda 171 Iramba 2 Isanju 204171002 1 1801 20 Mara 4 4 Bunda 181 Namhula 2 Kalukekele 204181002 1 3767 20 Mara 4 4 Bunda 191 Wariku 2 Kamukenga 204191002 1 2415 20 Mara 4 4 Bunda 201 Kabasa 1 Bitaraguru 204201001 1 3140 20 Mara 4 4 Bunda 201 Kabasa 4 Kabasa 204201004 1 2901 20 Mara 5 5 Musoma U 63 Bweri 1 Bweri 205063001 1 1298 20 Mara 5 5 Musoma U 83 Kigera 1 Kwangwa 205083001 1 745 20 Mara 5 5 Musoma U 133 Makoko 1 Bukanga 205133001 1 987 20 Mara 1 6 Rorya 201 Nyathorogo 2 Omuga 206201002 1 2309 20 Mara 1 6 Rorya 201 Nyathorogo 3 Nyasoko 206201003 0 1241 20 Mara 1 6 Rorya 211 Kisumwa 3 Marasibora 206211003 1 2066 20 Mara 1 6 Rorya 211 Kisumwa 4 Kwibuse 206211004 0 2511 127 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 20 Mara 1 6 Rorya 221 Rabour 1 Makongoro 206221001 1 3427 20 Mara 1 6 Rorya 231 Komuge 1 Iryenyi 206231001 1 3103 20 Mara 1 6 Rorya 231 Komuge 3 Komuge 206231003 0 2123 20 Mara 1 6 Rorya 251 Kyang'ombe 1 Baraki 206251001 1 3291 20 Mara 1 6 Rorya 251 Kyang'ombe 4 Ruhu 206251004 1 3950 20 Mara 1 6 Rorya 261 Kirogo 1 Radienya 206261001 0 1710 20 Mara 1 6 Rorya 261 Kirogo 2 Kirogo 206261002 1 2702 20 Mara 1 6 Rorya 271 Nyamagaro 3 Kyangasaga 206271003 1 4419 20 Mara 1 6 Rorya 281 Nyamtinga 1 Rwang'enyi 206281001 1 2982 20 Mara 1 6 Rorya 281 Nyamtinga 3 Busanga 206281003 0 3224 20 Mara 1 6 Rorya 291 Nyahongo 2 Ryagati 206291002 1 1962 20 Mara 1 6 Rorya 291 Nyahongo 5 Omoche 206291005 1 3293 20 Mara 1 6 Rorya 291 Nyahongo 6 Nyamkonge 206291006 0 1476 20 Mara 1 6 Rorya 301 Tai 2 Nyahera 206301002 1 2419 20 Mara 1 6 Rorya 321 Bukura 1 Kirongwe 206321001 1 3596 20 Mara 1 6 Rorya 321 Bukura 2 Bubombi 206321002 0 4116 20 Mara 1 6 Rorya 331 Roche 3 Osiri 206331003 0 1925 20 Mara 1 6 Rorya 341 Kitembe 1 Sakawa 206341001 1 3347 20 Mara 1 6 Rorya 351 Goribe 3 Panyakoo 206351003 0 3453 20 Mara 1 6 Rorya 371 Mirare 3 Malongo 206371003 0 1691 20 Mara 1 6 Rorya 381 Kigunga 1 Bukama 206381001 1 3745 20 Mara 1 6 Rorya 393 Koryo 1 Mang'ore 206393001 0 1078 20 Mara 1 6 Rorya 393 Koryo 2 Nyanduga 206393002 1 2531 21 Manyara 1 1 Babati 21 Mamire 1 Chemchem 211021001 1 1263 21 Manyara 1 1 Babati 21 Mamire 5 Endakiso 211021005 1 5021 21 Manyara 1 1 Babati 33 Gallapo 1 Ayamango 211033001 1 3486 21 Manyara 1 1 Babati 33 Gallapo 3 Gallapo 211033003 1 5296 21 Manyara 1 1 Babati 41 Qash 2 Majengo 211041002 1 1541 21 Manyara 1 1 Babati 41 Qash 4 Qash 211041004 1 4909 21 Manyara 1 1 Babati 61 Bonga 1 Endanachan 211061001 1 2767 21 Manyara 1 1 Babati 61 Bonga 4 Ayasamba 211061004 1 2190 21 Manyara 1 1 Babati 71 Gidas 5 Gidas 211071005 1 3347 21 Manyara 1 1 Babati 81 Duru 2 Endagwe 211081002 1 3593 21 Manyara 1 1 Babati 91 Riroda 2 Nakwa 211091002 1 4345 21 Manyara 1 1 Babati 91 Riroda 3 Riroda 211091003 1 5940 21 Manyara 1 1 Babati 101 Sigino 3 Dagailoy 211101003 1 2860 21 Manyara 1 1 Babati 111 Arri 4 Managha 211111004 1 4900 21 Manyara 1 1 Babati 123 Dareda 1 Seloto 211123001 1 6057 21 Manyara 1 1 Babati 123 Dareda 5 Gajal 211123005 1 2122 21 Manyara 1 1 Babati 131 Dabil 2 Maganjwa 211131002 1 5265 21 Manyara 1 1 Babati 141 Ufana 2 Luxmanda 211141002 1 3208 128 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 21 Manyara 1 1 Babati 151 Bashnet 3 Bashnet 211151003 1 5766 21 Manyara 1 1 Babati 151 Bashnet 5 Guse 211151005 1 3583 21 Manyara 1 1 Babati 161 Madunga 3 Madunga 211161003 1 4795 21 Manyara 1 1 Babati 171 Kiru 2 Malangi 211171002 1 1686 21 Manyara 1 1 Babati 183 Magugu 1 Sarame 211183001 1 1396 21 Manyara 1 1 Babati 183 Magugu 4 Masware 211183004 1 1569 21 Manyara 1 1 Babati 191 Magara 1 Mayoka 211191001 1 3929 21 Manyara 1 1 Babati 201 Mwada 1 Kisangaji 211201001 1 5915 21 Manyara 1 1 Babati 201 Mwada 2 Mwada 211201002 1 4983 21 Manyara 2 2 Hanang 11 Balangdalalu 1 Murumba 212011001 1 3064 21 Manyara 2 2 Hanang 11 Balangdalalu 2 Balangdalalu 212011002 1 5414 21 Manyara 2 2 Hanang 21 Gehandu 1 Ming'enyi 212021001 1 2618 21 Manyara 2 2 Hanang 21 Gehandu 3 Ishponga 212021003 1 4427 21 Manyara 2 2 Hanang 31 Laghanga 3 Laghanga 212031003 1 3219 21 Manyara 2 2 Hanang 41 Getanuwas 2 Getanuwas 212041002 1 4034 21 Manyara 2 2 Hanang 51 Hirbadaw 1 Mwanga 212051001 1 3310 21 Manyara 2 2 Hanang 51 Hirbadaw 2 Hirbadaw 212051002 1 3703 21 Manyara 2 2 Hanang 61 Bassodesh 1 Garawja 212061001 1 5532 21 Manyara 2 2 Hanang 61 Bassodesh 3 Bassodesh Part I 212061003 1 1206 21 Manyara 2 2 Hanang 73 Bassotu 2 Bassotu 212073002 1 5289 21 Manyara 2 2 Hanang 73 Bassotu 3 Mulbadaw 212073003 1 5640 21 Manyara 2 2 Hanang 81 Gendabi 1 Dawar 212081001 1 3897 21 Manyara 2 2 Hanang 91 Mogitu 1 Mogitu 212091001 1 5481 21 Manyara 2 2 Hanang 91 Mogitu 3 Jorodom 212091003 1 3030 21 Manyara 2 2 Hanang 101 Gitting 1 Barjomot 212101001 1 2902 21 Manyara 2 2 Hanang 101 Gitting 3 Gitting 212101003 1 3171 21 Manyara 2 2 Hanang 111 Masakta 2 Masakta 212111002 1 4148 21 Manyara 2 2 Hanang 133 Endasak 1 Endasiwold 212133001 1 3601 21 Manyara 2 2 Hanang 133 Endasak 3 Endagaw 212133003 1 2851 21 Manyara 2 2 Hanang 141 Gidahababieg 2 Endasabogeshan 212141002 1 1462 21 Manyara 2 2 Hanang 151 Measkron 3 Measkron 212151003 1 4296 21 Manyara 2 2 Hanang 161 Hidet 2 Hidet 212161002 1 3111 21 Manyara 2 2 Hanang 181 Sirop 1 Matangarimo 212181001 1 1825 21 Manyara 2 2 Hanang 191 Gisambalang 1 Gisambalang 212191001 1 2952 21 Manyara 2 2 Hanang 203 Nangwa 1 Nangwa 212203001 1 3619 21 Manyara 2 2 Hanang 203 Nangwa 2 Wareta 212203002 1 5187 21 Manyara 3 3 Mbulu 11 Daudi 2 Gandumehhi 213011002 1 4054 21 Manyara 3 3 Mbulu 21 Bargish 1 Antsi 213021001 1 4991 21 Manyara 3 3 Mbulu 21 Bargish 3 Bargish Uwa 213021003 1 1711 21 Manyara 3 3 Mbulu 31 Gehandu 2 Isawa 213031002 1 2159 21 Manyara 3 3 Mbulu 41 Kainam 2 Nahasey 213041002 1 2282 129 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 21 Manyara 3 3 Mbulu 41 Kainam 5 Hareabi 213041005 1 3103 21 Manyara 3 3 Mbulu 51 Murray 2 Kwermusil 213051002 1 3622 21 Manyara 3 3 Mbulu 51 Murray 4 Murray 213051004 1 4218 21 Manyara 3 3 Mbulu 61 Sanu 2 Ayamaami 213061002 1 3562 21 Manyara 3 3 Mbulu 81 Tlawi 1 Harbaghet 213081001 1 2689 21 Manyara 3 3 Mbulu 81 Tlawi 3 Masqaroda 213081003 1 4028 21 Manyara 3 3 Mbulu 91 Bashay 1 Harsha 213091001 1 5597 21 Manyara 3 3 Mbulu 91 Bashay 3 Muslurb 213091003 1 5303 21 Manyara 3 3 Mbulu 103 Dongobesh 1 Dongobesh 213103001 1 3153 21 Manyara 3 3 Mbulu 103 Dongobesh 3 Ngorat 213103003 1 2705 21 Manyara 3 3 Mbulu 111 Tumati 2 Yaeda - ampa 213111002 1 3432 21 Manyara 3 3 Mbulu 111 Tumati 4 Tumati 213111004 1 6080 21 Manyara 3 3 Mbulu 121 Maretadu 1 Qamtananati 213121001 1 2574 21 Manyara 3 3 Mbulu 121 Maretadu 4 Singu 213121004 1 2802 21 Manyara 3 3 Mbulu 121 Maretadu 7 Maretadu juu 213121007 1 4070 21 Manyara 3 3 Mbulu 131 Maghang 2 Labay 213131002 1 4185 21 Manyara 3 3 Mbulu 131 Maghang 4 Gidmadoy 213131004 1 2005 21 Manyara 3 3 Mbulu 143 Haidom 1 Harar 213143001 1 2747 21 Manyara 3 3 Mbulu 143 Haidom 4 Getanyamba 213143004 1 3321 21 Manyara 3 3 Mbulu 143 Haidom 6 Endahaghadat 213143006 1 2841 21 Manyara 3 3 Mbulu 161 Masieda 1 Masieda 213161001 1 3694 21 Manyara 3 3 Mbulu 161 Masieda 3 Endahagichan 213161003 1 2318 21 Manyara 4 4 Simanjiro 13 Orkesumet 1 Orkesumet 214013001 1 5277 21 Manyara 4 4 Simanjiro 23 Naberera 1 Okutu 214023001 1 1163 21 Manyara 4 4 Simanjiro 23 Naberera 2 Landanai 214023002 1 3589 21 Manyara 4 4 Simanjiro 23 Naberera 3 Naberera 214023003 1 3400 21 Manyara 4 4 Simanjiro 23 Naberera 4 Namalulu 214023004 1 2670 21 Manyara 4 4 Simanjiro 31 Loibor - Siret 1 Loibor 214031001 1 2577 21 Manyara 4 4 Simanjiro 41 Emboreet 1 Emboreet 214041001 1 2259 21 Manyara 4 4 Simanjiro 41 Emboreet 2 Loiborsiot 214041002 1 2797 21 Manyara 4 4 Simanjiro 51 Terrat 1 Loswaki 214051001 1 3082 21 Manyara 4 4 Simanjiro 51 Terrat 2 Terat 214051002 1 2969 21 Manyara 4 4 Simanjiro 51 Terrat 3 Komolo 214051003 1 4944 21 Manyara 4 4 Simanjiro 51 Terrat 4 Sukuro 214051004 1 2710 21 Manyara 4 4 Simanjiro 61 Oljoro N0. 5 2 Oljoro No. 5 214061002 1 1919 21 Manyara 4 4 Simanjiro 61 Oljoro N0. 5 3 Olborkishu 214061003 1 3220 21 Manyara 4 4 Simanjiro 61 Oljoro N0. 5 4 Lorokare 214061004 1 1874 21 Manyara 4 4 Simanjiro 71 Shambarai 1 Kilombero 214071001 1 1017 21 Manyara 4 4 Simanjiro 71 Shambarai 2 Olbili 214071002 1 3513 21 Manyara 4 4 Simanjiro 71 Shambarai 3 Shambarai 214071003 1 3094 21 Manyara 4 4 Simanjiro 83 Mererani 1 Naisinyai 214083001 1 2188 130 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 21 Manyara 4 4 Simanjiro 91 Msitu wa Tembo 1 Msitu wa Tembo 214091001 1 4712 21 Manyara 4 4 Simanjiro 91 Msitu wa Tembo 2 Msitu wa Tembo 214091002 1 1408 21 Manyara 4 4 Simanjiro 91 Msitu wa Tembo 3 Magadini 214091003 1 2885 21 Manyara 4 4 Simanjiro 91 Msitu wa Tembo 4 Nyorinyori 214091004 1 1211 21 Manyara 4 4 Simanjiro 101 Ngorika 1 Ngorika 214101001 1 2287 21 Manyara 4 4 Simanjiro 101 Ngorika 2 Nyumba ya Mungu 214101002 1 2091 21 Manyara 4 4 Simanjiro 101 Ngorika 3 Lemkuna 214101003 1 555 21 Manyara 4 4 Simanjiro 111 Loiborsoit 2 Ngage 214111002 1 2682 21 Manyara 5 5 Kiteto 21 Partimbo 1 Mbigiri 215021001 1 1853 21 Manyara 5 5 Kiteto 21 Partimbo 3 Namelok 215021003 1 5105 21 Manyara 5 5 Kiteto 21 Partimbo 4 Laalala 215021004 1 3421 21 Manyara 5 5 Kiteto 21 Partimbo 6 Partimbo 215021006 1 2170 21 Manyara 5 5 Kiteto 31 Njoro 1 Njoro 215031001 1 3903 21 Manyara 5 5 Kiteto 31 Njoro 3 Olpopong'i 215031003 1 2704 21 Manyara 5 5 Kiteto 43 Olbolot 1 Machiga 215043001 1 3899 21 Manyara 5 5 Kiteto 43 Olbolot 2 Olboloti 215043002 1 2726 21 Manyara 5 5 Kiteto 43 Olbolot 3 Kiperesa 215043003 1 630 21 Manyara 5 5 Kiteto 71 Kijungu 2 Kijungu 215071002 1 2459 21 Manyara 5 5 Kiteto 81 Lengatei 1 Lengatei 215081001 1 3786 21 Manyara 5 5 Kiteto 81 Lengatei 3 Lesoit 215081003 1 1581 21 Manyara 5 5 Kiteto 91 Sunya 1 Sunya - Kitongoji cha Mnadani 215091001 1 5592 21 Manyara 5 5 Kiteto 101 Dongo 1 Dongo - Kitongoji cha Chamwino 215101001 1 7795 21 Manyara 5 5 Kiteto 101 Dongo 2 Enguserosidani - Kitongoji cha 215101002 1 3373 21 Manyara 5 5 Kiteto 101 Dongo 3 Enguserosidani - Kitongoji cha 215101003 1 2928 21 Manyara 5 5 Kiteto 111 Songambele 2 Orgine - Kitongoji cha Mdunku 215111002 1 3461 21 Manyara 5 5 Kiteto 121 Dosidosi 1 Suguta 215121001 1 922 21 Manyara 5 5 Kiteto 121 Dosidosi 2 Dosidosi - Madukani 215121002 1 3290 21 Manyara 5 5 Kiteto 131 Engusero 1 Ndirigish - Mbande & Mbande Ml 215131001 1 3207 21 Manyara 5 5 Kiteto 131 Engusero 3 Engusero - Miremire & Mvugala 215131003 1 7218 21 Manyara 5 5 Kiteto 143 Matui 1 Chapakazi Kitongoji cha Msagar 215143001 1 3215 21 Manyara 5 5 Kiteto 143 Matui 2 Ositeti - Kitongoji cha Subug 215143002 1 1739 131 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 21 Manyara 5 5 Kiteto 143 Matui 3 Enguserongine - Kitongoji cha 215143003 1 1235 21 Manyara 5 5 Kiteto 143 Matui 4 Matui - Kitongoji cha Juhudi,M 215143004 1 5293 21 Manyara 5 5 Kiteto 153 Bwagamoyo 1 Bwagamoyo 215153001 1 1313 21 Manyara 5 5 Kiteto 153 Bwagamoyo 2 Kaloleni 215153002 1 2389 51 kaskazini 1 1 Kaskazini 21 Mto wa Pwani 11 Mto wa Pwani 511021011 1 355 51 kaskazini 1 1 Kaskazini 41 Kivunge 12 Kivunge 511041012 1 685 51 kaskazini 1 1 Kaskazini 51 Tumbatu Gomani 12 Kokoni/bunjuni 511051012 1 421 51 kaskazini 1 1 Kaskazini 51 Tumbatu Gomani 17 Munchore 511051017 1 252 51 kaskazini 1 1 Kaskazini 51 Tumbatu Gomani 42 Mtakuja 511051042 1 406 51 kaskazini 1 1 Kaskazini 61 Tumbatu Jongowe 13 Kidarini 511061013 1 387 51 kaskazini 1 1 Kaskazini 71 Mkwajuni 23 Kidombo 511071023 1 297 51 kaskazini 1 1 Kaskazini 71 Mkwajuni 43 Uyagu Msikitini 511071043 1 275 51 kaskazini 1 1 Kaskazini 81 Kibeni 24 Mpitile/Mji Mkubwa 511081024 1 260 51 kaskazini 1 1 Kaskazini 91 Muwange 15 Muwange 511091015 1 356 51 kaskazini 1 1 Kaskazini 111 Potoa 13 Potoa 511111013 1 460 51 kaskazini 1 1 Kaskazini 121 Fukuchani 21 Kichungwani/Kibondeni 511121021 1 513 51 kaskazini 1 1 Kaskazini 141 Tazari 11 Kishagani - Mkokosi 511141011 1 377 51 kaskazini 1 1 Kaskazini 141 Tazari 21 Kishagani - Mkokosi 511141021 1 440 51 kaskazini 1 1 Kaskazini 161 Nungwi 13 Mji kati/Banda kuu 511161013 1 516 51 kaskazini 1 1 Kaskazini 161 Nungwi 41 Muambale 511161041 1 470 51 kaskazini 1 1 Kaskazini 171 Matemwe 11 Mkungunini/Kichangajak u 511171011 1 477 51 kaskazini 1 1 Kaskazini 171 Matemwe 21 Kinazini 511171021 1 456 51 kaskazini 1 1 Kaskazini 171 Matemwe 62 Joga kuu/Mchonga 511171062 1 629 51 kaskazini 1 1 Kaskazini 181 Kijini 41 Putweni/mjaweka 511181041 1 440 51 kaskazini 1 1 Kaskazini 191 Pwani Mchangani 22 eneo la Mahoteli 511191022 1 627 51 kaskazini 1 1 Kaskazini 211 Moga 11 Vibanda Thineashara 511211011 1 412 51 kaskazini 1 1 Kaskazini 221 Chaani Masingini 21 Chaani/Mdogo 511221021 1 372 51 kaskazini 1 1 Kaskazini 241 Chaani Kubwa 12 Migombani 511241012 1 243 51 kaskazini 1 1 Kaskazini 241 Chaani Kubwa 32 chaani stand 511241032 1 475 51 kaskazini 1 1 Kaskazini 271 Kinyasini 12 Kidimini 511271012 1 449 51 kaskazini 1 1 Kaskazini 271 Kinyasini 41 Ngava 511271041 1 376 51 kaskazini 2 2 Kaskazini 11 Misufini 11 Mkongwe Hagewa 512011011 1 496 51 kaskazini 2 2 Kaskazini 11 Misufini 31 Mabuzini kask. 512011031 1 840 51 kaskazini 2 2 Kaskazini 11 Misufini 42 Muembe Mdema 512011042 1 826 51 kaskazini 2 2 Kaskazini 11 Misufini 62 Kiguruni 512011062 1 930 51 kaskazini 2 2 Kaskazini 11 Misufini 81 Michongomani 512011081 1 1312 51 kaskazini 2 2 Kaskazini 21 Makoba 21 Maruhubi 512021021 1 1444 51 kaskazini 2 2 Kaskazini 21 Makoba 32 Dundua 512021032 1 1232 132 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 51 kaskazini 2 2 Kaskazini 21 Makoba 43 Michungwani 'A' 512021043 1 988 51 kaskazini 2 2 Kaskazini 21 Makoba 51 Msikiti mkubwa 512021051 1 1198 51 kaskazini 2 2 Kaskazini 51 Kiomba mvua 11 Dola 512051011 1 984 51 kaskazini 2 2 Kaskazini 61 D/Mchangani 12 Bububu 512061012 1 886 51 kaskazini 2 2 Kaskazini 71 Mkadini 12 Kwa Darueshi 512071012 1 926 51 kaskazini 2 2 Kaskazini 81 Zingwe zingwe 11 Zingwe zingwe 512081011 1 1024 51 kaskazini 2 2 Kaskazini 91 Kitope 22 Kitopendani 512091022 1 1002 51 kaskazini 2 2 Kaskazini 91 Kitope 52 Kwa Gube 512091052 1 916 51 kaskazini 2 2 Kaskazini 103 Mahonda 11 Chechele nyumba za starling 512103011 1 878 51 kaskazini 2 2 Kaskazini 103 Mahonda 21 Mahonda 512103021 1 668 51 kaskazini 2 2 Kaskazini 103 Mahonda 31 Uwanja wa misuka 512103031 1 922 51 kaskazini 2 2 Kaskazini 121 Donge Mtambile 21 Ndunduke 512121021 1 798 51 kaskazini 2 2 Kaskazini 121 Donge Mtambile 25 Panga maua 512121025 1 1066 51 kaskazini 2 2 Kaskazini 131 Kinduni 13 Kinduni 512131013 1 748 51 kaskazini 2 2 Kaskazini 141 Donge Karange 13 Kiduka Kongwe 512141013 1 810 51 kaskazini 2 2 Kaskazini 161 Donge Kipange 11 Kilimo 512161011 1 636 51 kaskazini 2 2 Kaskazini 171 Donge Vijibweni 21 Kitaruni 512171021 1 946 51 kaskazini 2 2 Kaskazini 181 Upenja 11 Bwana Kaseme 512181011 1 954 51 kaskazini 2 2 Kaskazini 191 Kiwengwa 12 Kumba Urembo 512191012 1 1188 51 kaskazini 2 2 Kaskazini 211 Kilombero 11 geukeni Badi/Muembe 512211011 1 872 52 Kusini 1 1 Kati 13 Dunga Bweni 12 D/Bweni 521013012 1 438 52 Kusini 1 1 Kati 21 Ubago 12 Kidogo Basi 521021012 1 605 52 Kusini 1 1 Kati 31 Kidimini 14 Vijijini 521031014 1 561 52 Kusini 1 1 Kati 41 Machui 13 Machui 521041013 1 354 52 Kusini 1 1 Kati 61 Miwani 11 Miwani 521061011 1 448 52 Kusini 1 1 Kati 71 Kiboje Mkwajuni 11 Kiboje Mkwajuni 521071011 1 473 52 Kusini 1 1 Kati 81 Ghana 12 Ghana 521081012 1 520 52 Kusini 1 1 Kati 93 Koani 31 Mkahawa/Wajane 521093031 1 440 52 Kusini 1 1 Kati 111 Uzini 11 Uzini 521111011 1 416 52 Kusini 1 1 Kati 131 Tunduni 11 Tunduni 521131011 1 408 52 Kusini 1 1 Kati 141 Bambi 12 Bambi 521141012 1 523 52 Kusini 1 1 Kati 161 Umbuji 14 Umbuji 521161014 1 294 52 Kusini 1 1 Kati 171 Mchangani 15 Mchangani 521171015 1 378 52 Kusini 1 1 Kati 191 Ndijani 11 Ndijani 521191011 1 395 52 Kusini 1 1 Kati 191 Ndijani 15 Ndijani 521191015 1 581 52 Kusini 1 1 Kati 201 Jendele 11 Jendele 521201011 1 737 52 Kusini 1 1 Kati 211 Chwaka 12 Sokoni 521211012 1 556 52 Kusini 1 1 Kati 221 Marumbi 11 Marumbi 521221011 1 513 52 Kusini 1 1 Kati 231 Uroa 13 Uroa 521231013 1 329 52 Kusini 1 1 Kati 251 Jumbi 12 Jumbi 521251012 1 609 133 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 52 Kusini 1 1 Kati 271 Binguni 13 Binguni 521271013 1 555 52 Kusini 1 1 Kati 281 Cheju 14 Kibondemaji/Zuwiyani 521281014 1 272 52 Kusini 1 1 Kati 301 Unguja Ukuu/Kae Pwani 11 U/kae Pwani 521301011 1 469 52 Kusini 1 1 Kati 311 Kikungwi 11 Kikungwi 521311011 1 631 52 Kusini 1 1 Kati 321 Uzi 15 Uzi 521321015 1 466 52 Kusini 1 1 Kati 351 Ukongoroni 11 Ukongoroni 521351011 1 301 52 Kusini 1 1 Kati 371 Mpapa 12 Mpapa 521371012 1 457 52 Kusini 2 2 Kusini 13 Nganani 12 Tovu 522013012 1 410 52 Kusini 2 2 Kusini 31 Mzuri 11 Kae/Kuu/Sheba 522031011 1 443 52 Kusini 2 2 Kusini 31 Mzuri 13 Tasani 522031013 1 329 52 Kusini 2 2 Kusini 31 Mzuri 15 Mbuyuni 522031015 1 380 52 Kusini 2 2 Kusini 31 Mzuri 17 Mzuri Kaja 522031017 1 538 52 Kusini 2 2 Kusini 41 Kajengwa 12 Kiundwi 522041012 1 302 52 Kusini 2 2 Kusini 41 Kajengwa 14 Mbuyu Ng'ombe 522041014 1 416 52 Kusini 2 2 Kusini 51 Jambini kikadini 12 Kikadini 522051012 1 650 52 Kusini 2 2 Kusini 51 Jambini kikadini 14 Kikadini 522051014 1 474 52 Kusini 2 2 Kusini 61 Mtende 12 Vijijini 522061012 1 590 52 Kusini 2 2 Kusini 71 Kibuteni 11 Kibuteni 522071011 1 496 52 Kusini 2 2 Kusini 81 Kizimkazi/Dimbani 12 Kizimkazi/Dimbani 522081012 1 450 52 Kusini 2 2 Kusini 91 Kizimkazi/Mkunguni 11 Kijungu/Mnazi mmoja 522091011 1 531 52 Kusini 2 2 Kusini 91 Kizimkazi/Mkunguni 13 Hema/Kiungani 522091013 1 678 52 Kusini 2 2 Kusini 101 Muyuni 'A' 12 Muembe panda 522101012 1 517 52 Kusini 2 2 Kusini 111 Muyuni 'B' 12 Mabundi/Kikutani/Kijichi /Nyambizi 522111012 1 344 52 Kusini 2 2 Kusini 121 Muyuni 'C' 12 Mchangani 522121012 1 361 52 Kusini 2 2 Kusini 141 Muungoni 11 Muungoni 522141011 1 366 52 Kusini 2 2 Kusini 141 Muungoni 13 Muungoni 522141013 1 428 52 Kusini 2 2 Kusini 151 Paje 12 Paje 522151012 1 487 52 Kusini 2 2 Kusini 161 Jambiani Kibigija 11 j/Kibigija - Kichakanyuki 522161011 1 467 52 Kusini 2 2 Kusini 161 Jambiani Kibigija 13 j/Kibigija - Dimbuni 522161013 1 420 52 Kusini 2 2 Kusini 161 Jambiani Kibigija 22 j/Kibigija 522161022 1 410 52 Kusini 2 2 Kusini 171 Bwejuu 12 Bwejuu 522171012 1 604 52 Kusini 2 2 Kusini 171 Bwejuu 14 Bwejuu 522171014 1 452 52 Kusini 2 2 Kusini 171 Bwejuu 16 Bwejuu 522171016 1 523 52 Kusini 2 2 Kusini 181 Kitogani 12 Ofisi ya Jimbo 522181012 1 364 53 Mjini Magharibi 1 1 Magharibi 13 Mwera 11 Bonde la Mpunga 531013011 1 429 53 Mjini Magharibi 1 1 Magharibi 13 Mwera 32 Kimara 531013032 1 541 53 Mjini Magharibi 1 1 Magharibi 13 Mwera 51 Muembe Mchomeke 531013051 1 263 53 Mjini Magharibi 1 1 Magharibi 13 Mwera 62 Mtofaani 531013062 1 439 53 Mjini Magharibi 1 1 Magharibi 23 Mtoni 13 Mtoni 531023013 1 607 134 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 53 Mjini Magharibi 1 1 Magharibi 33 Bububu 22 Kidichi 531033022 1 485 53 Mjini Magharibi 1 1 Magharibi 33 Bububu 32 Bububu Meli Nane 531033032 1 248 53 Mjini Magharibi 1 1 Magharibi 41 Chuini 15 Chuini 531041015 1 523 53 Mjini Magharibi 1 1 Magharibi 51 Kama 14 Kama 531051014 1 486 53 Mjini Magharibi 1 1 Magharibi 71 Mwakaje 21 Kitundu 531071021 1 465 53 Mjini Magharibi 1 1 Magharibi 81 Fuoni Kibondeni 11 Chunga 531081011 1 555 53 Mjini Magharibi 1 1 Magharibi 81 Fuoni Kibondeni 61 Kipungani 531081061 1 428 53 Mjini Magharibi 1 1 Magharibi 81 Fuoni Kibondeni 72 Fuoni 531081072 1 551 53 Mjini Magharibi 1 1 Magharibi 91 kianga 17 Kianga 531091017 1 396 53 Mjini Magharibi 1 1 Magharibi 101 Dole 13 Dole 531101013 1 468 53 Mjini Magharibi 1 1 Magharibi 111 Kizimbani 31 Mkanyageni 531111031 1 570 53 Mjini Magharibi 1 1 Magharibi 121 Mbuzini 21 Mbuzini 531121021 1 1036 53 Mjini Magharibi 1 1 Magharibi 141 Maungani 12 Maungani/ Mtongani 531141012 1 407 53 Mjini Magharibi 1 1 Magharibi 151 Shakani 12 Shakani 531151012 1 745 53 Mjini Magharibi 1 1 Magharibi 173 Chukwani 11 Chukwani Vijijini 531173011 1 1165 53 Mjini Magharibi 1 1 Magharibi 173 Chukwani 21 Buyu 531173021 1 474 53 Mjini Magharibi 1 1 Magharibi 201 Dimani 21 Ndambani 531201021 1 372 53 Mjini Magharibi 1 1 Magharibi 211 Kombeni 22 Kombeni 531211022 1 654 53 Mjini Magharibi 1 1 Magharibi 243 Magogoni 12 Kinuni 531243012 1 527 53 Mjini Magharibi 1 1 Magharibi 253 Kidatu 13 Chumbuni Ndogo 531253013 1 332 53 Mjini Magharibi 1 1 Magharibi 273 Fuoni Kijitoupele 14 Chunga 531273014 1 310 53 Mjini Magharibi 1 1 Magharibi 273 Fuoni Kijitoupele 21 Kipungani 531273021 1 665 54 Kaskazini Pemba 1 1 Wete 21 Mtambwe Kaskazini 21 Uondwe 541021021 1 732 54 Kaskazini Pemba 1 1 Wete 21 Mtambwe Kaskazini 71 Jambaji 541021071 1 532 54 Kaskazini Pemba 1 1 Wete 31 Fundo 51 Uvinje Uland 541031051 1 194 54 Kaskazini Pemba 1 1 Wete 41 M/Mdogo 32 Kitambuu 541041032 1 453 54 Kaskazini Pemba 1 1 Wete 41 M/Mdogo 51 Jojo 541041051 1 436 54 Kaskazini Pemba 1 1 Wete 51 Kambini 12 Kambini 541051012 1 595 54 Kaskazini Pemba 1 1 Wete 51 Kambini 21 Hindi 541051021 1 723 54 Kaskazini Pemba 1 1 Wete 61 Kojani 21 Msikitini 541061021 1 572 54 Kaskazini Pemba 1 1 Wete 61 Kojani 25 Mpambani 541061025 1 676 54 Kaskazini Pemba 1 1 Wete 71 Ole 11 Ole Mjini 541071011 1 570 54 Kaskazini Pemba 1 1 Wete 71 Ole 44 Ole 541071044 1 443 54 Kaskazini Pemba 1 1 Wete 71 Ole 73 Kianga 541071073 1 404 54 Kaskazini Pemba 1 1 Wete 71 Ole 102 Uhanga 541071102 1 524 54 Kaskazini Pemba 1 1 Wete 91 Kiuyu 12 Kiuyu mjini 541091012 1 602 54 Kaskazini Pemba 1 1 Wete 91 Kiuyu 32 Maongweni 541091032 1 512 54 Kaskazini Pemba 1 1 Wete 91 Kiuyu 47 Kiuyu 541091047 1 463 54 Kaskazini Pemba 1 1 Wete 101 Piki 12 Mzambarauni 541101012 1 501 54 Kaskazini Pemba 1 1 Wete 111 Kisiwani 11 Shangafu 541111011 1 704 54 Kaskazini Pemba 1 1 Wete 111 Kisiwani 33 Kisiwani 541111033 1 606 135 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 54 Kaskazini Pemba 1 1 Wete 121 Gando 22 Junguni 541121022 1 485 54 Kaskazini Pemba 1 1 Wete 131 Ukunjwi 11 Raha 541131011 1 572 54 Kaskazini Pemba 1 1 Wete 131 Ukunjwi 41 Ukunjwi 541131041 1 652 54 Kaskazini Pemba 1 1 Wete 141 Pandani 41 Kijuki 541141041 1 375 54 Kaskazini Pemba 1 1 Wete 151 Shengejuu 12 Kiungoni 541151012 1 432 54 Kaskazini Pemba 1 1 Wete 151 Shengejuu 52 Shengejuu 541151052 1 406 54 Kaskazini Pemba 1 1 Wete 151 Shengejuu 81 Mtemani 541151081 1 516 54 Kaskazini Pemba 1 1 Wete 181 Mtambwe Kusini 61 Kivukoni 541181061 1 693 54 Kaskazini Pemba 2 2 Micheweni 13 Micheweni 12 Chamboni 542013012 1 656 54 Kaskazini Pemba 2 2 Micheweni 13 Micheweni 21 Micheweni 542013021 1 555 54 Kaskazini Pemba 2 2 Micheweni 13 Micheweni 64 Mjini Wingwi 542013064 1 670 54 Kaskazini Pemba 2 2 Micheweni 21 Msuka 13 Mtongwe 542021013 1 487 54 Kaskazini Pemba 2 2 Micheweni 21 Msuka 61 Gombani 542021061 1 553 54 Kaskazini Pemba 2 2 Micheweni 31 Kinowe 11 Jiso 542031011 1 451 54 Kaskazini Pemba 2 2 Micheweni 31 Kinowe 41 Pombwe 542031041 1 645 54 Kaskazini Pemba 2 2 Micheweni 41 Tumbe 11 Tumbe Mashariki 542041011 1 614 54 Kaskazini Pemba 2 2 Micheweni 41 Tumbe 17 Tumbe Mashariki 542041017 1 435 54 Kaskazini Pemba 2 2 Micheweni 41 Tumbe 24 Tumbe Magharibi 542041024 1 550 54 Kaskazini Pemba 2 2 Micheweni 41 Tumbe 43 Sizini 542041043 1 496 54 Kaskazini Pemba 2 2 Micheweni 51 Mgogoni 11 Mgogoni 542051011 1 415 54 Kaskazini Pemba 2 2 Micheweni 51 Mgogoni 41 Taifu 542051041 1 505 54 Kaskazini Pemba 2 2 Micheweni 61 Shumba Viamboni 21 Mgeni nje 542061021 1 586 54 Kaskazini Pemba 2 2 Micheweni 71 Finya 11 Finya 542071011 1 557 54 Kaskazini Pemba 2 2 Micheweni 83 Konde 11 K/Manda 542083011 1 643 54 Kaskazini Pemba 2 2 Micheweni 83 Konde 41 Matangatuani 542083041 1 470 54 Kaskazini Pemba 2 2 Micheweni 91 Wingwi Mapofu 22 Mapofu 542091022 1 456 54 Kaskazini Pemba 2 2 Micheweni 91 Wingwi Mapofu 42 Kandaani 542091042 1 447 54 Kaskazini Pemba 2 2 Micheweni 101 Kiuyu Maziwa N'gombe 16 Kiuyu 542101016 1 421 54 Kaskazini Pemba 2 2 Micheweni 101 Kiuyu Maziwa N'gombe 21 Kiuyu 542101021 1 384 54 Kaskazini Pemba 2 2 Micheweni 101 Kiuyu Maziwa N'gombe 37 Maziwa N'gombe 542101037 1 284 54 Kaskazini Pemba 2 2 Micheweni 111 Makangale 21 Kijijini 542111021 1 602 54 Kaskazini Pemba 2 2 Micheweni 121 Wingwi/Njuguni 11 Michungani 542121011 1 596 54 Kaskazini Pemba 2 2 Micheweni 121 Wingwi/Njuguni 23 Njuguni 542121023 1 523 54 Kaskazini Pemba 2 2 Micheweni 121 Wingwi/Njuguni 31 Mtakao 542121031 1 429 54 Kaskazini Pemba 2 2 Micheweni 131 Shumba Mjini 11 Shumba Mjini 542131011 1 537 55 Kusini Pemba 1 1 Chake 21 Wawi 11 Mjengo wa Banda 551021011 1 750 55 Kusini Pemba 1 1 Chake 21 Wawi 31 Mtemani 551021031 1 633 55 Kusini Pemba 1 1 Chake 21 Wawi 71 Ditia 551021071 1 706 136 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 55 Kusini Pemba 1 1 Chake 31 Pujini 21 kijili 551031021 1 539 55 Kusini Pemba 1 1 Chake 31 Pujini 41 Mchangani 551031041 1 448 55 Kusini Pemba 1 1 Chake 31 Pujini 71 Dodo 551031071 1 608 55 Kusini Pemba 1 1 Chake 41 Ziwani 11 Ziwani/Barawa 551041011 1 767 55 Kusini Pemba 1 1 Chake 41 Ziwani 24 Mbuzini 'A' 551041024 1 477 55 Kusini Pemba 1 1 Chake 51 Ndagoni 11 Ngaju 551051011 1 519 55 Kusini Pemba 1 1 Chake 51 Ndagoni 33 Ndagoni 551051033 1 621 55 Kusini Pemba 1 1 Chake 61 Kwale 21 Nyapi 551061021 1 686 55 Kusini Pemba 1 1 Chake 61 Kwale 51 Michungwani 551061051 1 247 55 Kusini Pemba 1 1 Chake 61 Kwale 81 Kukuchuni 551061081 1 387 55 Kusini Pemba 1 1 Chake 71 Vitongoji 31 Malikindi 551071031 1 360 55 Kusini Pemba 1 1 Chake 71 Vitongoji 43 Vikutani 551071043 1 576 55 Kusini Pemba 1 1 Chake 71 Vitongoji 54 Kibokoni 551071054 1 355 55 Kusini Pemba 1 1 Chake 81 Ngambwa 33 Vukunguni Buyuni 551081033 1 635 55 Kusini Pemba 1 1 Chake 91 Shungi 21 Kiziwani 551091021 1 354 55 Kusini Pemba 1 1 Chake 101 Chonga 11 Chanjamjawiri 551101011 1 468 55 Kusini Pemba 1 1 Chake 101 Chonga 23 Chonga 551101023 1 1028 55 Kusini Pemba 1 1 Chake 111 Mgelema 21 Ngomeni 551111021 1 531 55 Kusini Pemba 1 1 Chake 121 Kilindi 51 Tandaani 551121051 1 600 55 Kusini Pemba 1 1 Chake 161 Mvumoni 21 Mvumoni 551161021 1 452 55 Kusini Pemba 1 1 Chake 161 Mvumoni 41 KilimahodiKilimni 551161041 1 337 55 Kusini Pemba 1 1 Chake 171 Matale 23 Mwembe/Karata 551171023 1 378 55 Kusini Pemba 1 1 Chake 181 Wesha 61 Kiwandani 551181061 1 395 55 Kusini Pemba 1 1 Chake 191 Uwandani 13 Uwandani 551191013 1 614 55 Kusini Pemba 2 2 Mkoani 21 Makombeni 13 Makombeni 552021013 1 607 55 Kusini Pemba 2 2 Mkoani 51 Mkanyageni 22 Mkanyageni 552051022 1 621 55 Kusini Pemba 2 2 Mkoani 61 Michenzani 11 Mkadini 552061011 1 821 55 Kusini Pemba 2 2 Mkoani 61 Michenzani 51 Kizungu 552061051 1 748 55 Kusini Pemba 2 2 Mkoani 71 Chokocho 11 Ulenge 552071011 1 502 55 Kusini Pemba 2 2 Mkoani 71 Chokocho 71 Kandarani 552071071 1 463 55 Kusini Pemba 2 2 Mkoani 81 Kisiwa Panza 21 Panza - Mtajuu 552081021 1 563 55 Kusini Pemba 2 2 Mkoani 91 Kangani 22 Tanga/Barabarani 552091022 1 641 55 Kusini Pemba 2 2 Mkoani 91 Kangani 34 Kangani 552091034 1 626 55 Kusini Pemba 2 2 Mkoani 103 Kengeja 12 Mahuduthi 552103012 1 727 55 Kusini Pemba 2 2 Mkoani 103 Kengeja 41 Mapungwi 552103041 1 350 55 Kusini Pemba 2 2 Mkoani 111 Muambe 11 Chanjaani 552111011 1 644 55 Kusini Pemba 2 2 Mkoani 111 Muambe 31 Bwegeza 552111031 1 445 55 Kusini Pemba 2 2 Mkoani 111 Muambe 39 Bwegeza 552111039 1 536 55 Kusini Pemba 2 2 Mkoani 121 Kiwani 12 Kendwa/Vizuke 552121012 1 382 55 Kusini Pemba 2 2 Mkoani 121 Kiwani 34 Kiwani 552121034 1 508 55 Kusini Pemba 2 2 Mkoani 121 Kiwani 42 Nanguji 552121042 1 566 137 Region Code Region Name Old District code New District Code District Name Old Ward Code Ward Name EA / Village Code Street / Village Name Cluster ID In 2002/03 Census (1=Yes 0=No) 2002 Cluste r Popula tion 55 Kusini Pemba 2 2 Mkoani 133 Mtambile 51 Kigope/Mitunda Fumoni 552133051 1 598 55 Kusini Pemba 2 2 Mkoani 141 Mizingani 31 Ngagadu 552141031 1 606 55 Kusini Pemba 2 2 Mkoani 151 Ngwachani 31 Mtengombe 552151031 1 585 55 Kusini Pemba 2 2 Mkoani 161 Chambani 12 Tumbini 552161012 1 533 55 Kusini Pemba 2 2 Mkoani 161 Chambani 62 Chwale 552161062 1 339 55 Kusini Pemba 2 2 Mkoani 171 Wambaa 11 Kwaazani 552171011 1 651 55 Kusini Pemba 2 2 Mkoani 201 Mtangani 11 Kichaka 552201011 1 586 55 Kusini Pemba 2 2 Mkoani 201 Mtangani 22 Mtangani 552201022 1 495 55 Kusini Pemba 2 2 Mkoani 211 Ukutini 25 Ukutini 552211025 1 471 55 Kusini Pemba 2 2 Mkoani 221 Chumbageni 51 Chumbageni 552221051 1 419 138 Appendix II: Training of Trainers Training Schedule 139 Training of Trainers (TOT) Morogoro Centre 76 Participants Training of District Supervisors and Enumerators Training of Enumerators/DS Arusha Region 3 Trainers Arumeru District 1st Centre Arumeru District- 27 Arusha “U”- 4 Meru District- 27 DS’s - 6 Monduli District 2 nd. Centre Karatu District 3 rd. Centre Monduli District -27 Longido District - 27 DS.s - 4 Karatu District -27 Ngorongoro District -27 DS.s - 4 Training of Enumerators/DS Dodoma Region Kondoa District 1st Centre Kondoa District - 27 DS’s- 2. Mpwapwa District 2 nd. Centre Chamwino District 3 rd. Centre Mpwapwa District - 27 Kongwa District - 27 DS”s - 4 Chamwino District - 27 Dodoma “U” District - 27 Bahi District – 27 DS - 6 Training of Enumerators/DS Kilimanjaro Region Same District 1st Centre 3 Trainers Rombo District 2 nd. Centre Same District - 27 Mwanga District - 27 DS - 4 Rombo District - 27 Moshi “R” District - 27 DS - 4 Hai District - 27 Siha District – 27 DS - 4 Training of Enumerators/DS Tanga Region 3 Trainers Korogwe District 1st Centre Handeni District 2 nd. Centre Muheza District 3 rd. Centre Korogwe District - 27 Lushoto District - 27 DS - 4 Handeni District -27 Kilindi District - 27 DS-4 Muheza District - 27 Tanga District - 27 Pangani District -24 Mkinga District - 27 DS -8 68 61 61 32 61 91 61 61 61 61 61 Hai District 3 rd. Centre 116 140 Training of Enumerators/DS Mwanza Region 3 Trainers 1 st. Centre Geita District 2nd. Centre Ukerewe Didtrict 3rd. Centre Kwimba District Geita District - 27 Sengerema District - 27 DS- 4 Ukerewe Didtrict - 27 Ilemela Didtrict - 17 Nyamagana Didtrict - 0 DS -4 Magu District - 27 Kwimba District - 27 Misungwi District - 27 DS- 6 Training of Enumerators/DS Mara Region 3 Trainers 1 st. Centre Tarime District 2nd. Centre Serengeti District 3rd. Centre Bunda District Tarime District -27 Rorya District - 27 DS- 4 Serengeti District - 27 DS- 2 Bunda District -27 Musoma “R” District -27 Musoma “U” District - 3 DS- 6 Training of Enumerators/DS Manyara Region 3 Trainers 1 st. Centre Babati District 2nd. Centre Kiteto District 3rd. Centre Simanjiro District Babati District - 27 Hanang District - 27 Mbulu District – 27 DS- 6 Kiteto District - 27 DS- 2 Simanjiro District - 27 DS - 2 Training of Enumerators/DS Coast Region 3 Trainers 1 st. Centre Bagamoyo District 2nd. Centre Mkuranga District 3rd. Centre Mafia District Bagamoyoo District -27 Kibaha District -27 DS- 4 Rufiji District -27 Kisarawe District -27 Mkuranga District- 27 DS- 6 Mafia District -20 DS - 2 Training of Enumerators/DS D’Salaam Region 3 Trainers 1 st. Centre Temeke District – 23 DS-2 2nd. Centre Ilala District – 10 DS -1 3rd. Centre Kinondoni District - 19 DS - 2 Ilala District 61 51 90 61 32 66 90 32 32 61 90 25 60 141 Training of Enumerators/DS Tabora Region 3 Trainers 1 st. Centre Urambo District 2nd. Centre Sikonge District 3rd. Centre Nzega District Urambo District -27 Tabora “U” District - 27 DS - 4 Sikonge District Uyui District DS- 4 Nzega District - 27 Igunga District- 27 DS- 4 Training of Enumerators/DS Kigoma Region 3 Trainers 1 st. Centre Kigoma “R” District 2nd. Centre Kasulu District 3rd. Centre Kibondo District Kigoma “R” District - 27 Kigoma “U” District – 5 DS - 3 Kasulu District – 27 DS- 2 Kibondo District - 27 DS- 2 Training of Enumerators/DS Shinyanga Region 3 Trainers 1 st. Centre Kishapu District 2nd. Centre Maswa Dstrict 3rd. Centre Kahama District Maswa District -27 Meatu District - 27 Bariadi District -27 DS- 6 Kahama District - 27 Bukombe District- 27 DS- 4 Training of Enumerators/DS Kagera Region 3 Trainers 1 st. Centre Misenyi District 2nd. Centre Muleba District 3rd. Centre Biharamulo District Karagwe District -27 Misenyi District -27 Bukoba “U” District- 8 DS-5 Muleba District -27 Bukoba “R” District- 27 DS- 4 Biharamulo District - 27 Ngara District -27 Chato District- 27 DS- 6 61 61 61 38 32 32 86 90 61 70 61 90 142 Training of Enumerators/DS Iringa Region 3 Trainers 1 st. Centre Kilolo District 2nd. Centre Mufindi District 3rd. Centre Njombe District Kilolo District - 27 Iringa “R” District -27 Iringa “U” District- 6 DS- 5 Mufindi District- 27 Njombe “R” District -27 DS- 4 Njome “U” District- 27 Ludewa District- 27 Makete District- 27 DS- 6 Training of Enumerators/DS Mbeya Region 3 Trainers 1 st. Centre Mbozi District 2nd. Centre Kyela District 3rd. Centre Mbeya “R” District Mbozi District- 27 Ileje District- 27 Mbeya “U” District- 24 DS- 6 Kyela District - 27 Rungwe District- 27 DS- 4 Mbeya “R” District - 27 Mbarali District- 27 Chunya District- 27 DS- 6 Training of Enumerators/DS Rukwa Region 3 Trainers 1 st. Centre S’wanga “R” District 2nd. Centre Nkasi District 3rd. Centre Mpanda District S/’wanga “R” District - 27 S/’wanga “U” District – 27 DS- 4 Nkasi District- 27 DS-2 Mpanda District -27 DS- 2 Training of Enumerators/DS Singida Region 3 Trainers 1 st. Centre Singida “R” District 2nd. Centre Iramba District 3rd. Centre Manyoni District Singida “R” District - 27 Singida “U” District - 18 Iramba District – 27 DS- 2 Manyoni District – 27 DS- 2 68 61 90 87 61 90 61 32 32 48 32 32 143 Training of Enumerators/DS Morogoro Region 3 Trainers 1 st. Centre Kilombero District 2nd. Centre Kilosa Didtrict 3rd. Centre Turiani Centre (Mvomero) District Kilombero District - 27 Ulanga District- 27 DS-4 Kilosa District - 27 Morogoro “U” – 25 DS - 4 Mvomero District - 27 Morogoro “R” – 27 DS- 4 Training of Enumerators/DS Lindi Region 3 Trainers 1 st. Centre Nachingwea District 2nd. Centre Mnazi Mmoja (Lindi “R”) District 3rd. Centre Kilwa District Nachingwea District - 27 Liwale District - 27 Rwangwa District – 27 DS- 6 Lindi “R” District - 27 Lindi “U” District – 6 DS- 3 Kilwa District – 27 DS-2 Training of Enumerators/DS Mtwara Region 3 Trainers 1 st. Centre Masasi District 2nd. Centre Newala Didtrict 3rd. Centre Mtwara “R” District Masasi District - 27 Nanyumbu District- 27 DS-4 Newala District - 27 Tandahimba Dist.-27 DS- 4 Mtwara “R” District - 27 Mtwara “U” District – 6 DS - 3 Training of Enumerators/DS Ruvuma Region 3 Trainers 1 st. Centre Tunduru District 2nd. Centre Namtumbo District 3rd. Centre Mbinga District Tunduru District – 27 DS- 2 Songea “R” District - 27 Namtumbo District – 25 DS- 4 Mbinga District - 27 Songea “U” District - 17 DS - 4 61 59 61 90 39 32 61 61 39 32 59 144 Appendix III A list of supervisors 145 APPENDIX III: TENTATIVE LIST OF TRAINERS FOR ENUMERATORS AND DISTRICT SUPERVISORS S/N Name Organization Region Designation Phone Number HQS Driver QCT Drive r 01 Mussa J. Muganda NBS Dodoma RSM '0754454443' Joseph Waya STJ 4043 02 Abraham, B. RS Dodoma PAO I '0754880482' 03 Upendo M. Mndeme MAFC Dodoma AO '0755849568' 04 Mwanaidi Abdalah MAFC Arusha RS '0754466814' Nil 05 Margaret M. Martin NBS Arusha RSM '0755373111' 06 Allen Mweta MOWI Arusha Senior Engineer '0784913478' 07 Alex Luhwavi NBS Kilimanj aro RSM '0754848954' Nil 08 S.B. Lyimo RAS Kilimanj aro RS '0754096057' 09 Demetria Ngilwa NBS Kilimanj aro Statisticial Officer 784261807 10 Tonny Mwanjota NBS Tanga RSM '0713755965' Nil 11 A.F. Kallaghe RAS Tanga PAFO '0784629008' 12 William Matee NBS Tanga Senior Statistician '0754516662' 13 Suma Tebela NBS Morogor o RSM '0756627828' Gogfrey Nyabukika STJ 4092 14 E. Masangya RAS Morogor o RS '0754026770' 15 Nsiima M.P.L. MLD&F Morogor o PLO I '0784300014' 16 Ibrahim Masanja NBS Pwani Senior Statistician '0784471189' Nil 17 S.B. Kashangaki RAS Pwani PAFO '0754834018' 18 Msike, Charles L. NBS Pwani Statistician '0713894951' 19 Magreth Maganda NBS Dar es Salaam RSM '0713231135' Nil 20 Rachel Tuvana RS Dar es Salaam RAA/AAS '0713218261' 21 Joyce Urasa NBS Dar es Salaam PST 0754360258' 22 Moses A. Sagala NBS Lindi RSM '0754279581' Nil 23 May Kiluwasha RAS Lindi RS '0754887267' 24 Jovitha Rugemalila NBS Lindi Statistician '0786239050' 25 Simon E. Semindu NBS Mtwara RSM '0754979024' Shomari Matewele STJ40 26 26 Dr. Abdu Hayghaimo RS Mtwara PVO-I '0754894218' 27 Kalekezi B MAFC Mtwara P/ECON '0755433290' 146 28 John Lyakurwa NBS Ruvuma RSM '0754844366' Nil 29 Mohamed Waziri RAS Ruvuma Ag.RS '0754937232' 30 Emanuel Mashenene NBS Ruvuma IT '0712272276' 31 Jonas A. Mdundo NBS Iringa RSM '0755765945' 32 Shenal Nyoni RS Iringa RS '0754309327' 33 Devotha Mdete NBS Iringa Statistician '0755255095' 34 Thesresia Lyimo NBS Mbeya RSM '0754823544' Nil 35 Alinanuswe A. Mwalwange Statistician Mbeya Statistician '0755748525' 36 Tabwene, D MAFC Mbeya P/A '0754823148' 37 M.O. Mahanakh KILIMO Singida RS '0784869394' Nil 38 Nestory S. Mazinza NBS Singida RSM '0713495663' 39 Jocelyn Rwehumbiza NBS Singida Statistician '0784923438' 40 G.O. Mwambanga MAFC Tabora RS '0734115144' Nil 41 Ernest E. Mshana NBS Tabora RSM '0754870512' 42 Maphito A.H. NBS Tabora SSO '0754360482' 43 Adam Ramadhani NBS Rukwa RSM '0784605820' Michael Madembwe STK 5502 44 Hamza Mvanz RS Rukwa RS '0754612308' 45 Robert S. Fundi MAFC Rukwa PE '0785974844' 46 Bayona P.L. NBS Kigoma RSM '0754403201' David Mwaisenye STK 5418 47 M.M. Muura RAS Kigoma RS '0784479740' 48 Paskas A. Sawaki NBS Kigoma Statistician '0754919198' 49 Goodluck Lyimo NBS Shinyan ga RSM '0713356969' Zuberi Mkawa STK 5140 50 Rajabu Masanche RAS Shinyan ga RS '0713657236' 51 Godfrey Temba NBS Shinyan ga SSO '0713428487' 52 R.K. Kagombola KILIMO Kagera RS '0755757217' Nil 53 Idd A. Muruke NBS Kagera RSM '0754697765' 54 Gambamala L.M. NBS Kagera Senior Statistician '0784625394' 55 Masanja, D.M. NBS Mwanza RSM '0784382626' Simon Milanzi STK 5500 56 Chotta A. RAS Mwanza RS '0755252355' 57 Oswald Ruboha MAFC Mwanza PE '0754882005' 58 Ramadhani Mbega NBS Mara RSM '0754482280' Nil 59 Edward Magoti RAS Mara RS '0782998828' 60 Kilian Paul NBS Mara IT '0713576355' 147 61 Juma Shabani NBS Manyara RSM 0754-0786 203565 Nil 62 Shayo C.V. RS Manyara PAO I '0784725909' 63 Elias Masunga MAFC Manyara P/L '0786229757' Quality Control Team Joyce Urasa and Ibrahim Masanja Dar es Salaam, Tanga, Arusha, Manyara Juma Gwau STJ 4021 Didas Tabwene Mbeya, Iringa, Ruvuma, Rukwa Rajab Kinanda STK 5456 Lubili Marco Gambamala Kagera, Kigoma, Tabora Alex Luoga STK 5139 Oswald Ruboha and Jocylyn Rwehumbiza Mwanza, Mara, Shinyanga, Singida Overrall Supervisor Said Aboud Saimon Minja STK 4670 148 APPENDIX III: DISTRIBUTION OF FIELD SUPERVISORS FOR ZANZIBAR 2007/2008 SAMPLED EA Region District Number of Enumeration Area (EA) Number of Enumerators Number of District Supervisor Number of Regional Supervisor Number of Field Supervisor North Unguja North 'A' 40 22 1 1 4 North 'B' 30 17 1 3 South Unguja Central 30 17 1 1 3 South 27 15 1 3 Urban West West 40 22 1 1 4 Sub Total (Unguja) 167 93 5 3 17 North Pemba Wete 40 22 1 1 4 Micheweni 40 22 1 4 South Pemba Chakechake 30 17 1 1 3 Mkoani 40 22 1 4 Sub Total (Pemba) 150 84 4 2 15 Grand Total 317 177 9 5 32 Average EA per Enumerator 2 149 Appendix IV Census Instruments 150 CONFIDENTIAL ACLF 1 Page Number………….. out of……………… Sub-village /ward leader listing from Comments (3) (5) (1) (2) (4) District _____________________Code Village ________________________ Code Sub village leader Number Name of Ward village leader Number of Households Form Office Register After enumeration UNITED REPUBLIC OF TANZANIA Agriculture Sample Census 2007/08 Region ______________________Code Ward _______________________Code 151 ACLF 2 Page Number………….. out of……………… Household listing from-for listing hh heads and agriculture activities Region Code District Code Name of sub village leader Ward Code Name of sub village___________________________________________ Village Code (1) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (2) Total Bulls Cows Calves Sheep Pigs Kuku/Bata/ Rabbit UNITED REPUBLIC OF TANZANIA Agriculture Sample Census 2007/08 Household number Household head name Number of If the Respondent Qualifies X Farmer Serial Number Fields a Cattle Goats CONFIDENTIAL 152 ACLF 3 Region Code ward : code Namba Sawia District village code Hatua Code (1) (5) (6) (7) (8) (9) (10) (11) Poutry (2) (3) (4) Cattle Goat Sheep Pigs UNITED REPUBLIC OF TANZANIA National Agriculture Sample Census 2007/08 Household listing for 15 selected farmers S/N Sub-village leader Number Name of sub-village leader Name of selected head of household Name of a Househol d Head Number of Field CONFIDENTIAL 153 154 155 1.0 IDENTIFICATION DETAILS 1.1 Na. 1.1.1 Rgion …………………………………………………………………… 1.1.2 District …………………………………………………………………… 1.1.3 Ward …………………………………………………………………… 1.1.4 …………………………………………………………………… 1.2 Deatails of the respondent or household head Na. 1.2.1 Name and number of local leader 1.2.2 Name and number of household head ……………………………………….. 1.2.3 Sex of household head 1.2.4 Name of respondent ……………………………………….. 1.2.5 Relationship of Respondent to household head 2.0 ACTIVITIES OF THE HOUSEHOLD 2.1 Typeof Agriculture Household Codes Location Location Name Codes Village Household agricultural activities codes(Q 2.1) Crops only.………...1 Livestock only ……....2 Pastoralist…….…3 Crops and Livestock ……....4 Relationship to household head codes (Q 1.2.5) Head of Household ………......1 Son /Daughter……..........3 Grandson/Granddaughter……............5 No relationship…….7 Spouse…………...…..2 Father/Mother……...4 Other relatives…...6 Identification 156 Read and Write (Col 8) Any other language: Must be a written language. For someone who can read and write in Kiswahili and any other language apart from English, the correct code is 1. For one who can read and write in English and any other language apart from Kiswahili the the correct code is 2. Code 4 should only be used for any other language which is not English or Kiswahili. Relation to head (Col 2): Household Head: A person who is acknowledged by all other members of the household either by virtue of their age or standing as the household head. Education Level Reached (Col 10): Ask the respondent the highest educational level reached. This aims at establishing whether at the time of enumeration the member of the household is studying has completed or has never studied. Make further enquiry for the level of education reached for those who have completed studies. Establish if the member had attained any training after graduation for the purposes for completing column number 9. For those who still continue attending studies during the period of this survey, establish their learning stage. For instance for a household member who studied up to Standard Three but did complete his/her education at this level, then his/her highest education level reached is Standard Two. For those indicated under code 3 (not studied) in column 8 should be marked code 99 (Not applicable) in column 9. Section 3.0 Note Make sure that you define the hh proper to ensure that all the members of the hh are included. Ensure that you stress that the hh is not just the hh heads direct family and that it includes other people living and eating together with the family. If you notice that the hh is large or you see many people around the hh and you have been given a smaller number of the hh members, make further enquiries until you are sure that you have captured all the hh members. Section 3.0 Household information. ii) For each household member complete columns 1,2,3 and 3 After completing columns 1, 2, 3 and 3 for each household member, go back to the first household member and complete the remaining columns for that member. iii) Repeat step 2 for the rest of the household members. Definition and working page for page 2 Question Specific Definitions: 157 HOUSEHOLD INFORMATION 3.0 HOUSEHOLD INFORMATION 3.1 Give details of personal particulars of all hh members beginning with hh head Ex Sex Start Na. with M = 1 hh Head F = 2 Mother Father (2) (3) (5) (6) (7) (8) (9) (11) 01 1 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 Not applicable for children under 5 Age Marit al Status Parental Survival Reard and Write Education status Levek of On farm engagem ents Main act …………...… Names of hh members ( 98 years or more enter 97, under one year old write 00) education (Start with hh Head) attained (4) (10) (12) …………...… (1) …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… …………...… Identification 158 CODES FOR Q3: HOUSEHOLD INFORMATION Off-farm Income (Col 13) These are income made from activities NOT on the HH’s farming activites. This can be from formal employmenbt (e.g. in gpvrenment etc.), temporary jobs, casual labourers and income generation activity and includes working for cash on other people’s farms. Indicate whether each member was involved in an off farm income generating activity during 2007/08 ................ Main activity (Col 12) Crop farming: ………………..01. Livestock farming/herding: ….02. Pastoralist …………………….03 Fishing ………………………..04 Fish farming ………….……….05 Paid employment / Government/parastal……06 Private/NGOs ………….07 Self employee (Off- farm cativities) - With employees ………...08 - Without employees ……...09 Non paid household member (off – farm activities) ……10. Unemployed but available for work ….11 Unemployed but unavailable for work..12 House mother …………………………13 Student ………………………….….14 Unable to work too old, too young, retired, disabled,child 15 Others (specify) …………………......98 Education Level (Col 10) Primary education Secondary Education Below Standard One.......00 Form One...............................11 Standard One ................01 Form Two ...............................12 Standard Two..................02 Fomr Three...........................13 S tandard Three...........03 Form Four ............................... 14 S tandard Four..............04 Form Five ................................15 S tandard Five...............05 Form Six ..................................16 S tandard S ix ...............06 Training after Seo.ondary Ed.....17 S tandard Seven............07 University and other Tertiary Ed...8 DarasS tandard E ight..08 Adult Education..........................19 Training after Primary Ed...09 Not apllicable .......................99 Pre Form One...............10 Relationship to household head (Col 2) Head of household.......1 Female/Male…...…..….2 Son/Daugther….…....3 Father/Mother……....…4 Grandson/daughter.…5 Other Relatives…..........6 Ed.ucation Level(Col 9) Studying ………………….1 Has completed….………...2 Never been to school ...…3 Involvement in farming activitie (Col 11) Works on farm full time.…..1 Works on farm part time.….2 Rarely works on farm....….3 Never works on farm.....…. 4 Reading and writing (Col 8) Kiswahili……………............………….1 English ………………..................……2 Kiswahili and English….......................3 Lugha nyingine…………...............…...4 Canno tread or write..........................….5 Survival of Parents( Col 6 & 7) Yes.....…1 No …..........2 Dont't know ....…….…….3 Marrital Status(Col 4) Married................……….….1 Single..................….……..…2 Co-habiting ..........................3 Divorced Separated...... …….…...…...4 Widow/widower....…………..5 159 Overview to section 4 S ection 4.0: Preliminary note L and Access/Ownership Land access/ownership refers to the area utilized by the members of the household. This does not include communal land where the resources are shared between household members. It does not include official communal land that the household has sole access to for example a plot for crop farming in the communal area. S ection 4.2: L and Use 1. Ask the respondent the area of the different land use categories the household has sole access to (Q4.2.1 to 4.2.12) and record in the appropriate spaces. 2. Add up the area of the different categories of land and compare it with the total area obtained in section 4.0. The total area should be the same. 3. If the total area is different find out which one is correct and make amendments where appropriate. Section 4.2 Land Use Temporary crops: are sown and harvested during the same agricultural year Permanent crops: are crops once sown or planted last for some years and need not to be replanted after each annual harvest. Permanent crops /mixed crops: This is a mixture of permanent and seasonal crops. The two crops can either be randomly planted together or in a particular pattern e; for example intercropping (1 row of maize and 1 row of beans). A field that has been divided into plots for different crops is not mixed). This is further subdivided into: Mixture of Permanent crops – two or more permanent crops grown tougher Mixture of Permanent and Temporary crops – permanent crop and annual crop together Mixture of Temporary crops– two or more temporary, annual crops grown together Pasture land: this is an area of owned/allocated land which is set aside for livestock grazing. It can be improved pasture where the farmer has planted grass, applied fertilized or where other means have been applied to improve the pasture. Or it can be natural pasture. Natural Bush: Land which has naturally grown shrubs and trees and is considered productive but is not utilized for farming or livestock production. Section 4.0 – Land Ownership 1. Ask the respondent if he knows the total areas of land the household has sole access to. If he knows make a note in the calculation space 2 Ask the respondent the area of the different land ownership categories the household has sole access to (Q4.1, 1 to 4.1.7) and record in the appropriate spaces. 3. Add up the area of the different categories of land and compare it with the total area obtained in step 1 (if the respondent provided the information) 4. If the total area is different find out which one is correct and make Definitions for Key Specific Questions Section 4.1 – Land Access/Ownership These are areas that were used by the households for the 2007/08 farming season Lease/Certificate of Ownership: Area under lease/certificate of ownership refers to the areas which were issued by the government. The household possesses government issued leasehold little or certificate of ownership. The land will normally be officially surveyed and boundaries marked. This includes leased land bought from others where the lease/certificate of ownership has been transferred. Customary Law: This refers to the land which the household does not have an official government but its right of use is granted by the traditional leaders. Bought: This refers to the areas of customary land that has been bought from others. This land does not have an official title and therefore is not leasehold. Rented from others: Land rented from others for cash or for a fixed amount in crop produce (e.g. fixed number of bags at harvest). Borrowed: use granted by land owner free of charge. Land owner can either be a lease holder or has right of access through customary law. Share cropping: where the household is permitted to use land which is then paid for from a percentage of the harvested crop Procedures for questions Definitions and working page for page 3 Overview to section 4 160 4.0 LAND ACCESS/OWNERSHIP/TENURE 4.1 LAND ACCESS/OWNERSHIP/TENURE Give details on Area owned by the household during 2007/08 agricultural season. Give area as reported by the respondent in acres 4.1.8 4.1.1 Area under certificate of ownership 4.1.2 Area owned under customary law 4.1.3 Area bought 4.1.9 4.1.4 Area rented from others 4.1.5 Area borrowed from others 4.1.6 Area share cropped from others 4.1.10 4.1.7 Area under other forms of tenure Total area 4.2 LAND USE Area used by the household for various agricultural activities during 2007/08 agricultural season 4.2.1 Area planted temporary monocrops 4.2.2 4.2.3 Area planted permanent moncrops 4.2.4 4.2.5 4.2.6 Area under pasture 4.2.7 Area under fallow 4.2.8 Area under natural forest 4.2.9 Area planted trees 4.2.10 Area rented to others 4.2.11 Area unsuitable for agricultrure 4.2.12 Uncultivated arable land (minus area under fallow) Area planted temporary mixed crops (e.g. maize and beans) Total area Area planted permanent mixed crops (e.g. banana, coffee, trees) Area planted permanent and temporary mixed crops (e.g. maize and banana) Area in Acre Area in acre Do you consider to have enough land for your household? (Yes=1, No=2) Is there any female who owns land or has customary rights to land ownership in this household? (Yes=1, No=2) Enter area as reported by the respondent in acres Was the whole household area used during the 2007/08 agricultural season? (Yes=1, No=2) Working space for calculations Identification . . . . . . . . . . . . . . . . . . . . . 161 Working table for the calculation area for annual mixed crops Permanent crop 1 Permanent crop 2 Permanent crop 3 Permanent crop 4 The remaining area for temp crops Name of the crop temp/permanent 1 Name of the crop temp/permanent 2 Name of the crop temp/permanent 3 Check total area Check total area for temporary crops plant Permanent crop 1 Permanent crop 2 Permanent crop 3 Permanent crop 4 The remaining area for temp crops Name of the crop temp/permanent 1 Name of the crop temp/permanent 2 Name of the crop temp/permanent 3 Check total area Check total area for temporary crops Total Area for mixed crops Total area for permanent crops Total area for mixed crops Total area for permanent crops Mixed crops plants (a) (b) (c) Crop Name for plants number of plants Total area of mixed (acre) Area Total Total area (acre) (a) (b) (c) (d) Mixed crops 1 (acre) of plants Name of the plant for plants Total area mix (acre) (f)=(d)*(e) % of temporary Area for permanent crop Total area (e) Area for Total (acre) (acre) of (d) (e) (f)=(d)*(e) % of temporary Area for temporary crop 0.000 . 0.000 0.000 0.000 0 . 0 . 0 . 0 . 0 . . . . . 0.000 . 0.000 0.000 0.000 0 . 0 . 0 . 0 . 0 . . . . . . . . . . . . Planted Area: Area in acre the household was able to plant Harvested Area: Area in acre the household was able to harvest a large portion of harvests . this is the same as the area planted minus the area that was destroyed by floods/ pets / Crop Codes(Creal / Tubers/ Roots: Code Crop 11 Maizei 12 Paddy 13 Sorghum 14 Buirush Millet 15 Finger Millet 16 Wheat 17 Barley 22 Sweet Potatoes 23 Irish Potatyoes 24 Yams 25 Cocoyamsi 26 Onions 27 Gingeri Crop Codes Legumes and Oil Code Crop 31 Beans 32 Cowpeas 33 Green Gram 34 Chick Peas 35 Dengu 36 Bambara nuts 37 Njegere 41 Sun flower 42 Simsim 43 Ground uts 47 Soya beans 48 Caster Seed Vegetable Codes: Code Crop 86 Cabbage 87 Tomatoes 88 Spinach 89 Carrot 90 Chillies 91 Amaranths 92 Pumpkin 93 Cucumber 94 Egg plant 95 Water mellon 96 Cauliflower 06 Melllon 05 nyanyachungu 02 Ocra 03 Radish 01 Green Beans 04 Bizari Cash crop codes: Code Crop 50 Cotton 51 Tobacco 53 Payrethrum 62 Jute 19 Seaweed Temporary/Annual Crops Crops planted and harvested within 12 months after which time the plants die . Most annual crops are planted and harvested on a seasonal base. Instructions for calculating the area of mixed crops in a mixture A. If the mixed crop is mixed annual ly only enter the total area of the field in the remaining area under temporary Crop and go to step one of these instructions. B. If the mixed crop is mixed permanent and annual try to work tyhe percent age taken by the different crops and calculate the area of annual crops outlined in step 1. Otherwise use the number of trees method to calculate the area of annula crops in the mix. C: Number of trees method to calculate annual crop areas in a permanent-annual crop mix.: (i) List each of the permanent crop in collumn b and enter the ground area per acre for each permanent crop ( from instrcutions for page 8) in colum d. (ii) Enter the number of permanent trees in the mix in collumn e as will be provided to you by the respondent (iii) Calculate the area occpied by each crop by multiplying collumn d and collumn e and sum up these to obatin the total area of permanent crops in the mix. iv) To obatin the area for tempofrary crops , substract (-) the area fro permanent crops from thne total area of crop mix and enter the resulst in in the total area under temporary crops. (v) Proceed to step 1 to calculate the area under each temporary crop. 1. Enter the name of each temporary crop in tyhe crop mix and estimate percentages of each crop. 2. Using the percentage for each crop, calculate the are for each crop from the remaining area under tenmporary crop. 3. After completing the exrcise for all the fields, sum the area of each crop in tyhe mix plus any monocrops and uenter the totals in section 5.1.1 Collumn 3. 4. Once the quantity harvested is obtained , caklculate the yields (metric tonnes/acre) and compare the figures with the norms given in the crops code box. If there is significantly differentce, check the area and the amouint harvested.. Definitions and working page for page 4 . . 162 5.0 PERMANENT AND TEMPORARY CROP PRODUCTION 5.1 ANNUAL CROPS AND VEGATBLE PRODUCTION-SHORT RAINY SEASON Did your household palnted any crop duding short rainy season for 2007/08 agricultural year? Yes = 1, No = 2,(If the answer is yes proceed to Section 5.3) 5.1.1 Provide the following details for each crop planted during the short rainy season for 2007/08 agricultural year Quant ity Quantity used Meas urem ent Quantity used (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) …………………. …………………. …………………. …………………. …………………. …………………. …………………. …………………. …………………. Total area planted Cost Name of Crop Quant ity Quantity used Quantity Cost (Tshs) Cultiv ated area Tyep of fertili sers used Planting Main crop owner: Enetr the number of the hh member from page 2 on informati on for hh members Pembejeo Crop code Actual area plnated (acre) Use of Seeds Irriga ted area Use of fertilisers (If 6 is the answer in col 11 proceed to col 16) Use of chemicals agaisnt weeds (If 6 is the answer in col 11 proceed to col 20) The type of seed plant ed Cultiv ated areaE neo lililot umik a Qunaity of agrochemicals Use of seeds (1) (2) (3) Quantity of fertilisers Coist (Ths) Main crop owner: (Col 4) Enter number of hh member from page 2 on details on hh members in Q. 3 Use of agricultural seeds ( Col 6,) For the whole crop..............1 3/4 of the whole crop..…......2 1/2 of tyhe whole crop..……..3 1/4 ofd the whole crop..……..4 Under 1/4 of the whole crop...5 Qunatity ( Col 7) Kg …….1 Seedlings....2 Gram…..3 Type of fertilsers ( Col 12) Organic fertiliser………...1 inorganic fertlisers…....2 Quantity ( Col 17) Kig …….1 Litre.........2 Gram…..3 Millilitre…..6 Use of farm inputs ( SCol10,11 & 16) For the whole crop..............1 3/4 of the wholrecrop..…......2 1/2 of tyhe whole crop..……..3 1/4 ofd the whole crop..……..4 Under 1/4 of the whole crop...5 Not used ……….…….6 Type of seeds planted ( Col 5) Local seeds …1 Improved seeds..……....2 Kipimo ( S/wima 13) Kilo …....1 Lita........2 Milli-lita..3 Identificatoion ● ● ● ● ● ● ● ● ● ● 163 164 Working area/calculation space Storage (Col. 30, Q 5.1.1): - Traditionally Made strcutures: The design of storage structures villagers have inherited from forefathers . - Improved Traditionally made structures: The design of tradional storagesrutures improved through modern technology. Marketing Challenges Q 5.1.1 Col. 33: - Farmers' Association: Village farmers who came together and started an association for the puporses of purchasing inputs/selling/storage of crops aiiming at fetching better prices. - Cooperative Union: A large inter-village/community set up in the district/ region or at national level for providing inputs, markets and storage of farmers' crops. - Government Regulatory laws for crops marketing: Government instituted laws for regulating transportation and selling of crops. Q 5.1.1 Col 31 1. For each of crops listed indicate major marketing problems for 2007/2008 agricultural season. Q 5.1.1. Instructions on crops storage: 1. For the listed crops establish whether or not the household stored crops for 2007/2008 agricultural season. 2. For the listed crops give explanations on storage. Inputs (Q 5.1.1) Farm Yard Manure: An organics fertliser made on farm from animal dung. . Compost: An organic fertiliser made on farm from decomposed plant materials. Insectcides: This is the chemical usde in protecting plants or killing pests. Fungicides: Protects plants from fungi attack. Herbicide: Chemicals used to control or kills weeds. Improved seeds: Scientifically attested to be suitable for agricultural use. Crops storage is keeping/reserving crops in a container or a special place for future use. Definitions and working page for page 5 Questions specific definitions 165 166 Working table for the calculation area for annual mixed crops Permanent crop 1 Permanent crop 2 Permanent crop 3 Permanent crop 4 The remaining area for temp crops Name of the crop temp/permanent 1 Name of the crop temp/permanent 2 Name of the crop temp/permanent 3 Check total area Check total area for temporary crops plant Permanent crop 1 Permanent crop 2 Permanent crop 3 Permanent crop 4 The remaining area for temp crops Name of the crop temp/permanent 1 Name of the crop temp/permanent 2 Name of the crop temp/permanent 3 Check total area Check total area for temporary crops Total area for mixed crops Total area for permanent crops (a) (b) % of temporary Area for temporary crop (d) (e) (f)=(d)*(e) Mazao mchanganyiko 2 (acre) plants (acre) % of temporary Area for permanent crop Total area mix (acre) Area for Total Total area Name of (e) (f)=(d)*(e) Total Area for mixed crops Total area for permanent crops (a) (b) (c) (d) Mixed crops 1 (acre) of plants Crop Name for plants number of plants Total area of mixed (acre) Area Total Total area (acre) (c) the plant of for plants 0.000 . 0.000 0.000 0.000 0 . 0 . 0 . 0 . 0 . . . . . 0.000 . 0.000 0.000 0.000 0 . 0 . 0 . 0 . 0 . . . . . . . . . . . . Planted Area: Area in acre the household was able to plant Harvested Area: Area in acre the household was able to harvest a large portion of harvests . this is the same as the area planted minus the area that was destroyed by floods/ pets / Crop Codes(Creal / Tubers/ Roots: Code Crop 11 Maizei 12 Paddy 13 Sorghum 14 Buirush Millet 15 Finger Millet 16 Wheat 17 Barley 22 Sweet Potatoes 23 Irish Potatyoes 24 Yams 25 Cocoyamsi 26 Onions 27 Gingeri Crop Codes Legumes and Oil Code Crop 31 Beans 32 Cowpeas 33 Green Gram 34 Chick Peas 35 Dengu 36 Bambara nuts 37 Njegere 41 Sun flower 42 Simsim 43 Ground uts 47 Soya beans 48 Caster Seed Vegetable Codes: Code Crop 86 Cabbage 87 Tomatoes 88 Spinach 89 Carrot 90 Chillies 91 Amaranths 92 Pumpkin 93 Cucumber 94 Egg plant 95 Water mellon 96 Cauliflower 06 Melllon 05 nyanyachungu 02 Ocra 03 Radish 01 Green Beans 04 Bizari Cash crop codes: Code Crop 50 Cotton 51 Tobacco 53 Payrethrum 62 Jute 19 Seaweed Temporary/Annual Crops Crops planted and harvested within 12 months after which time the plants die . Most annual crops are planted and harvested on a seasonal base. Instructions for calculating the area of mixed crops in a mixture A. If the mixed crop is mixed annual ly only enter the total area of the field in the remaining area under temporary Crop and go to step one of these instructions B. If the mixed crop is mixed permanent and annual try to work tyhe percent age taken by the different crops and calcualet the area of annual crops outlined in step 1. Otherwise use the number of trees method to calculate the area of annula crops in the mix. C: Number of trees method to calculate annual crop areas in a permanent-annual crop mix.: (i) List each of tyhe permanent crop in collumn b and enter the ground area per acre for each permanent crop ( from instrcutions for page 8) in colum d. (ii) Enter the number of permanent trees in the mix in collumn e as will be provided to you by the respondent (iii) Calculate the area occpied by each crop by multiplying collumn d and collumn e and sum up these to obatin the total area of permanent crops in the mix. iv) To obatin the area for tempofrary crops , substract (-) the area fro permanent crops from thne total area of crop mix and enter the resulst in in the total area under temporary crops. (v) Proceed to step 1 to calculate the area under each temporary crop. 1. Enter the name of each temporary crop in tyhe crop mix and estimate percentages of each crop. 2. Using the percentage for each crop, calculate the are for each crop from the remaining area under tenmporary crop. 3. After completing the exrcise for all the fields, sum the area of each crop in tyhe mix plus any monocrops and uenter the totals in section 5.1.1 Collumn 3. 4. Once the quantity harvested is obtained , caklculate the yields (metric tonnes/acre) and compare the figures with the norms given in the crops code box. If there is significantly differentce, check the area and the amouint harvested.. Definitions and working page for page 6 . . 167 5.3 PERMANENT/PERENNIAL CROPS AND FRUIT TREE PRODUCTION Does your household have any permanent/perennial crops or fruit trees Yes =1, No = 2, (If answer is NO proceed to Section 6.0) 5.3.1 Give details on permanent/perennial crops or fruit trees Quant ity Used (1) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) …………………..……… …………………..……… …………………..……… …………………..……… …………………..……… …………………..……… …………………..……… …………………..……… …………………..……… Production Section Mixed crops Monocrops Name of permanent/perennial crop crop code of permane nt / perennial crop/frui t trees Area for trees/seedling/bra nch/bushes Number of Tplants/ trees in the crop mixh of permanent and perennial crop Are for mixed crops Farm inputs Main crop owner: Enetr the number of the hh member from page 2 on informati on for hh Irriga tion Size Uses of Fertilisers (If 6 is the answe Area used Quantity o fertiliser (k The type of fertilis er used Uses of seeds Cost (Ths) Cultiv ated area Type of plant ed seeds (2) (3) (4) (Acre) Area culltivated ( col. 8) For the whole crop..............1 3/4 of the whole crop..…......2 1/2 of tyhe whole crop..……..3 1/4 ofd the whole crop..……..4 Under 1/4 of the whole crop... Type of seed planted ( Col 7) Local seeds...............1 Improved seeds........2 Dont't know/ Not applicable...3 Type o 14) Organi Qunatity ( Col 9) Kg …….1 Seedlings....2 Gram…..3 Use of farm inputs ( Col 12 & 13) For the whole crop..............1 3/4 of the wholrecrop..…......2 1/2 of tyhe whole crop..……..3 1/4 ofd the whole crop..……..4 Under 1/4 of the whole crop...5 Not used 6 Main crop owner (Col 6): nter the number of the hh member from page 2 on information for hh members in Q 3 Identification ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 168 5.3 PERMANENT/PERENNIAL CROPS AND FRUIT TREE PRODUCTION CONTINUED ….. I 5.3.1 Give details on permanent/perennial crops or fruit trees during 2007/08 agricultural year (17) (18) (19) (20) (21) (22) (23) (24) (25) (26) (27) (28) ……………………. ……………………. ……………………. ……………………. ……………………. ……………………. ……………………. ……………………. ……………………. Uses of weeds control chemical (If 6 is the naswer in col 17 Proceed to col 21) Area used Size Cost Quant ity Used Area used Quantity harvested (kg) Harvested area (acre) Quantity of mature plants Use of pesticides (If 6 is the answer in col 25 proceed to col 29) Crop harvesting and storage Cost Size Used Quantity stored (k Quant ity Area used Quant ity Use of fungicides (If 6 is the answer in col 20 proceed to col 24) Size Used Cost (1) (2) Name of crop Crop code (29) (30) (31) (32) Marketing problems (Col 35) Very low prices….............01 No problem ................11 No transport……….......02 Others (Specify ...........98 High transport costs.......03 Not applicable ......99 Lack of crop buyers .......04 Markets located far away ..05 Problems with farmers Associations 06 Probloems with cooperative Unions ....7 Problems with Businessmen Association ...8 Strigent Government Conditions ...9 L k f k ti i f ti 10 Main S torage mechanis ms (C ol 33) Locall storage facilities… … … … … .… ..1 Improved Local storage facilitiiies ...........2 Modern store… ....… … … … … ........… ..3 Open drums/sacks............ ..........… ..4 C ealed drums.… ...................… … … … ..5 In heaps.............................................6 not S tored...........................................7 Other means ()S pecify.........… … … … … .....8 Area us ed ( C ol 20&24) For the whole crop..............1 3/4 of the wholrecrop..… ......2 1/2 of tyhe whole crop..… … ..3 1/4 ofd the whole crop..… … ..4 Under 1/4 of the whole crop...5 d Quantity ( C ol 18, 22, & 26) Kig … … .1 Litre.........2 Gram… ..3 Millilitre… ..6 Identification ● ● ● ● ● ● ● ● ● 169 Working area/calculation space Storage (Col. 30, Q 5.2.1): - Traditionally Made strcutures: The design of storage structures villagers have inherited from forefathers . - Improved Traditionally made structures: The design of tradional storagesrutures improved through modern technology. Marketing Challenges Q 5.2.1 Col. 33: - Farmers' Association: Village farmers who came together and started an association for the puporses of purchasing inputs/selling/storage of crops aiiming at fetching better prices. - Cooperative Union: A large inter-village/community set up in the district/ region or at national level for providing inputs, markets and storage of farmers' crops. - Government Regulatory laws for crops marketing: Government instituted laws for regulating transportation and selling of crops. Q 5.2.1 Col 33 1. For each of crops listed indicate major marketing problems for 2007/2008 agricultural season. Q 5.2.1. Instructions on crops storage: 1. For the listed crops establish whether or not the household stored crops for 2007/2008 agricultural season. 2. For the listed crops give explanations on storage. Inputs (Q 5.2.1) Farm Yard Manure: An organics fertliser made on farm from animal dung. . Compost: An organic fertiliser made on farm from decomposed plant materials. Insectcides: This is the chemical usde in protecting plants or killing pests. Fungicides: Protects plants from fungi attack. Herbicide: Chemicals used to control or kills weeds. Improved seeds: Scientifically attested to be suitable for agricultural use. Crops storage is keeping/reserving crops in a container or a special place for future use. Definitions and working page for page 7 Questions specific definitions 170 Permanent Crops: These are crops once planted last longer in the farm and need not be replanted after each annual harvest. Most of the permanent plants include tress such as coconut tress, apple trees, grape trees, banana trees, pineapple trees etc. Number of Trees: These include manure trees and premature trees. Number of mature plants: A total of fruit bearing tress (e.g. mango trees, orange trees, avocado trees e.t.c). Instructions for permanent monocrops and crop mix: A. For a field with permanent monocrop enter farm size in collumn. 3. B. For a field with a permanent crop mix or a temporary crop mix, enter the number of trees only in collumn 4. C. For a field with a permanent crop mix /temporary annual crops , either: -Enter the area in collumn 4, if the total arae for permanent crops was obatined through calcualtion of percentages of each crop OR Enter the number of tree in collumn 5, if the number of plants/ seedlings of permanent crops was excluded Permanent crops:( crop oils) Code Crop Area per crop 44 Palm Trees 0.00049 45 Coconut tree 0.00037 46 Cashew nut tress 0.00062 Permanent crops: Code Crop Area per crop 70 Passion Fruit 0.00074 71 Bananas 0.00037 72 Avocado 0.00099 73 Mango 0.00099 74 Pawpaw 0.00037 76 Orange 0.00074 77 Grape fruit 0.00074 78 Grape 0.00012 79 Mandarin 0.00074 80 Guava . 0.00074 81 Plums 0.00074 82 Apples 0.00074 83 Peaches 0.00074 84 Mifyoksi 0.00074 85 Lime/lemon 0.00074 68 Pomelo 0.00099 69 Jack Fruit 0.00074 97 Durian 0.00074 98 Bilimbi 0.00074 99 Rambutan 0.00074 67 Bread Fruit 0.00099 38 Malay apple 0.00074 39 Star Fruit 0.00074 (Sakua) Permanent crops ( Cash crops) Code Crop Area per crop 53 Sisal 0.00012 54 Coffee 0.00049 55 Tea 0.00037 56 Cocoa 0.00049 57 Rubber 0.00099 58 Wattle 0.00099 59 Kapok 0.00124 60 Sugar-cane 0.00012 61 Cardamon 0.00049 63 Tamarin 0.00099 64 Cinarmon 0.00124 65 Nutmeg 0.00099 66 Clove 0.00074 18 Black pepper 0.00037 34 Pigeon Peas 0.00025 21 Cassava 0.00019 75 Pineapple 0.00006 86 Lemon Grass 21 Cassava: Cassava is a temporary crop, in order to simplify data collection on areas of production, data on cassava will be collected from areas under permanent crops. Definitions and working page for page 8 171 Working area/calculation space Storage (Col. 33, Q 5.3.1): - Traditionally Made strcutures: The design of storage structures villagers have inherited from forefathers . - Improved Traditionally made structures: The design of tradional storagesrutures improved through modern technology. Marketing Challenges Q 5.3.1 Col. 35: - Farmers' Association: Village farmers who came together and started an association for the puporses of purchasing inputs/selling/storage of crops aiiming at fetching better prices. - Cooperative Union: A large inter-village/community set up in the district/ region or at national level for providing inputs, markets and storage of farmers' crops. - Government Regulatory laws for crops marketing: Government instituted laws for regulating transportation and selling of crops. Q 5.3.1 Col 35 1. For each of crops listed indicate major marketing problems for 2007/2008 agricultural season. Q 5.3.1. Instructions on crops storage: 1. For the listed crops establish whether or not the household stored crops for 2007/2008 agricultural season. 2. For the listed crops give explanations on storage. Inputs (Q 5.3.1) Farm Yard Manure: An organics fertliser made on farm from animal dung. . Compost: An organic fertiliser made on farm from decomposed plant materials. Insectcides: This is the chemical usde in protecting plants or killing pests. Fungicides: Protects plants from fungi attack. Herbicide: Chemicals used to control or kills weeds. Improved seeds: Scientifically attested to be suitable for agricultural use. Definitions and working page for page 9 Questions specific definitions 172 Irrigated farming: Section 6.5: Source of irrigation water (Col 1): The main source of the water used for irrigation. Method of obtaining water (Col 2): The mechanism by which the water is extracted from the source Irrigatable area (Col 3): The area the irrigation system is designed to cover in acrage Area of irrigated land during the 2007/08 (Col 5): Area of land under irrigation during the 2007/08 agricultural year. This is the actual area nd NOT the cumulative areas recultivated in 2 or more cropping seasons. Q 6.5 Irrigation. 1. If a household uses irrigated farming give explanations aon source and method of obatining water. . 2. See Col 10, Q. 5.1.1 and 5.2.1 and Col 12, Q 5.3.1 to see if irrigation was applied to any crop. Investment in agriculture Investment activities: Investment activities refer to medium to long term farm development structures and projects. This can be irrigation structures, erosion conrol and water harvesting structures or other permanent or semi-permanent investment made on the land that the household owns. Section 6.2 Use of draft animals Animals used in agricultural activities by the household during 2007/08 agricultural season. Castrated Bulls: Castrated oxen meant for use in agricultural production. Uncastrated Bulls: mature bulls used for garicultrural activities but are not castrated. Cow: Farmers also use mature female cattle in agricultural activities due to shortage of bulls Donkey: Mature Male or female donekys are also used for agricultural production. Farm inputs: Sections 6.3 and 6.4 1. Collumn 2 Indicate whether or not inputs were used. 2. Compelte collumn 3 by indicating where the inouts were obatined and collumn 4 by indicating the distance from where the inputs were obatined Compost: An organic fertiliser made on farm from decomposed plant materials. Insectcides: This is the chemical usde in protecting plants or killing pests. Fungicides: Protects plants from fungi attack. Herbicide: Chemicals used to control or kills weeds. Improved seeds: Scientifically attested to be suitable for agricultural use. Farm implements, Q 6.1: 1. Collumn 2 Indicate whether or not inputs were used 2. Complete collumn 3 by entering the number of inputs used. Farm Implements (Col. 1): Machette : Includea all implements use in tree cutting namely cicle, et.c. Sprimkler: The pump carrued on the back or a hand used water pump Hand used small tractor: A small tractor used in cultivation while the user walks on foot (see photo). Definitions and working page for page 10 173 6.2.6 6.3 USE OF ORGANIC FERTILISERS Cows Donkeys Type of fertiliser Used Yes=1, No=2 Quantit y Shredding Machine (1) (2) (3) (4) (5) Power Tiller 6.3.2 Manure 6.3.3 Compost Name of inputs (4) Compost IRRIGATED FARMING Did the household use irrigated farming during 2007/08 agriculture year? Yes=1, No = 2 If the answer is yes proceed to Section 6.6 Na. 6.5.2 Source Inorganic fertilisers Area that can be irrigated (Acre) Quantity used Area used (Acre) Used (Yes=1, No=2) Distance (3) Give details on inputs used during 2007/08 agricultural year (3) (1) (2) Improved seeds (2) (1) Insecticides/Fungicide Pest and weeds control chemicals Uncastrated bulls Tractor tiller Main source of obtaining water Main source of water for irrigation Oxen pulled plough for making terraces Area irrigated during 2007/08 agriculture year (Acre) ACCES TO INPUTS Tractor hallow Farm yard manure Castrated bulls Give details on the use of organic fertlisers during 2007/08 agriculture year Power Tiller 6.3.1 (4) Source (Col.3) Government.….......................01 Cooperative Union…... ...02 Farm inputs store/market.......03 Auction..............................04 Development project…….....05 Corp buyers…........06 Large Scake farms….......07 Made by the household.......08 Form neighbour...........................09 Cooperative Union…….....10 Others .....……….............98 Not applicable.................99 Distance from the source (Cola 4 ) Under 1 kilometre………….…......1 Btween One and three kilometres ......2 Btween three and 10 killometres3 Between 10 and 20 Kilometres .......4 Over 20 Kilometres......………….........5 Not applicable..........................................9 Means of obtaining water(C0l2) Flwoing. (gravity)...….…………...1 Using a bucket….…………………….....2 Water pump (using hand or leg)...………...3 Electric /fuel driven pump/ mafuta……………..4 Other (Specify).….....……………………….8 Source of irrigation water (Col 1) River…………………1 Wells …………………..…..4 Lake ………………2 Deep wells………….…… .5 Dams.…………….3 Cannals ….…………………. .6 Tape water……..…… …7 ● ● ● KQuantity (Col 3) Kg...….……1 Ton………...2 ● ● 174 Q 6.6 The type of erosion contro/Water harvesting (Col 1) Terraces: Structures constructed on mountain slopes to provide flat terrain for crop planting. Erosion control bunds: these are bunks of earth/stones built perpendicular to the slope to slow dowm the speed of water and thus preventing soil erosion. Its differs from terraces in that the soils on these banks are not at ground level . Gabions: A box like structure made of wire and filled with large stones to prevent gully errosion. Sand bags: Are used in controlling and preventing gully errosion Tree belt/wind breaks: Trees planted against the wind direction for breaking wind speed.. Section 7.0 Acces to credit for crop or livestock production Credit refers to something provided in cash or in kind (such as farm inputs, machines, livestock and other things) for crop or livestock production. The value of the credit must be repaid back to the lender. An Interest may or may not be attached to the value of the credit The credit may be repaid either in cash or through farm produce to be harvested . In this question the enumerator is at liberty to inquire up to three sources of credit where the farmer accessed credit from more than one source. Section 7.0 Source of agriculture credit If tghe farmer obtained credit from more than one source the use the code from the list provided. Start with the main source of credit in Section "7.1.1".a Q 6.6 Number of water harvestin structures and year of construction 1. The number water haversting structures refers to the number of wokring / maintained structures and does not include derelict or iireparable structures. 2. Year of construction refers to the year in which the structures were built, and not the year the structures were last repaired.The year should be written in figures e.g. 1998, 2006. Section 8.0 Agricultural extension services 1. Ask if the household did receive agricultural extension services during 2007/08 agricultural season from the respondents listed in collumn 1, then enter column 2. 2. Complete all columns for every extension officer. Section 8.0 Agricultural Extension Services Agricultural Extension Services: Refers to educational services provided to farmers by exetsion officers for the purposes of increasing crop and livestock production. Share-cropping: Refers to farming where smallholder / Smallscale farmer enters into an agreement with large scale farmer where the former sells produce to the latter in exchange of provisions of farm inputs and the like. . Contract farming Farming: Farming agreement entered between smallscale and large scale farmerswith regards to markets of farm produce and provision of farm inputs Definitions and working page for page 11 175 6.6 SOIL EROSION 6.6.1 Did the household experience soil erosion during 2007/08 agriculture year? (Yes=1,No=2) 6.6.2 Na. 6.6.3 6.6.7 Tree belt 6.6.4 6.6.8 6.6.5 6.6.9 Trenches 6.6.6 6.6.10 Other 7.0 7.1 SELECT UP TO THREE SOURCES AND PROCEED TO QUESTIONA 8.0 Source of credit 7.1.1a 7.1.2a 7.1.3a Credit provided to 7.1.1b 7.1.2b 7.1.3b (Male=1, Female=2) 7.2 IF THE ANSWER TO QUESTION 7.1 IS NO Give reasons for not accessing credit 8.0 ADVISORY SERVICES IN AGRICULTURE 8.1 8.2 Na. Advise on agriculture (3) 8.3.1 Spacing 8.3.2 Use of agrochemicals 8.3.3 Soil erosion control 8.3.4 Use of organic manure 8.3.5 Matumizi ya mbolea za viwandani 8.3.6 Use of improved seeds 8.3.7 Use of modern farm implements 8.3.8 Irrigation 8.3.9 Crop Storage 8.3.10 Pest control 8.3.11 Other (Specify) (3) Terraces (2) Is there any household member who accessed on farm credit during 2007/08 agriculture year? Yes=1, No=2 (If answer is NO, Proceed to Section 7.2) (3) (1) (1) (2) Source of advise Soil bunks of water harvesting Did the household participate in the contract farming during 2007/08 agriculture year? (Yes=1, No=2) Gabions/sand bags Bunks for erosion control ACCESS TO ON FARM CREDITS Did the household participate in outgrowers scheme during 2007/08 agriculture year? (Yes=1, No=2) Vetiva leaves (2) Did your household receive agricultural advise on the following : (IF THE ANSWER IS NO IN COL 2 PROCEED TO THE FOLLOWING QUESTION (1) Rceived advice (Yes=1, No=2) Did the household applied any methods for erosion contro/water harvesting during 2007/08 agricultural year? Mechanisms of controlling erosion/ Water harvesting Number of water harvesting Year of construction Type of erosion control/water harvesting Year of construction Number of water harvesting (Yes=1, No =2) (If the answer is No, Proceed to Section 7.0) (Source of credit Q 7.1.1, 7.1.2, 7.1.3) Relative...... 1 Saccos....4 NGO/Development projectsi........7 Bank... ……......................2 Busineman/Shop................5 Cooperative Union...........3 Priviate individuaks...............................6 Other...............9 Source of agricultural advice (Cokl. 3) Government……1 NGO/Development project.....2 Cooperative….3 Large Scale farmer….4 Ratdio/Newspapers….5 Neighbour ..........6 Other source………..8 Reasons for not accessing credit (Q 7.2)COL Not required …........1 Did not to be indebted...........3 Did nott know how to access credit......5 Credit delayed......7 Did not credit existed.....9 Not available ..............2 High interest rates......4 Bureaucracy.............................................6 Other (Specify)...........8 Identification 8.3 176 Section 9.3 Goat Note: Question 9.3 is for the actual number of owned or raised by the household (as of 1st October 2008) This number does not include g oats kept on behalf by relatives or neig hbours, that is the g oat outside the residential area of the household under survey. 1. If the household has she goats, you would normally expect them to have kids Type of cattle (sectioin 9.1.1 to 9.1.7) Bull: Mature uncastrated made cattle used for breeding Cow: Mature female cattle that has given birth at least once Ox: Castrated made cattle used for farm work Steer: Castrated made cattle us ed for meat Heifer: Female cattle of 1 year up to the first calving Q 9.1 and 9.3 : What is required is to establish whether or not the household kept or raised the listed livetsock during 2007/08 agricultural season (i.e. from October 2007 to September 2008). Also to establish the number of livestock as of 1st October 2008 Keeping or raising livestock is to to keep livestock at home while providing the livestock with animal feeds and medication and other services. The livestock could be owned by the farmer or kept on behalf of relatives or neighbours . Sections 9.1.1 to 9.1.7 Cattle Note: Q 9.1 is for the actual number of cattle owned or kept by the household (as of 1st October 2008). This number does not include herds of cattle kept on behalf by relatives or neighbours; that is, the cattle outside the residential area of the household under survey. 1. If the the household keep mature fecund female cattle, it is expected that such a household will have calves which will be entered in question 9.1.6 or 9.1.7 Type of Goat (Qs 9.3.1 to 9.3.5) Billy Goat (he-goat): Mature Uncastrated male goat used for breeding Castrated goat: Male goat that has been castrated She Goat: Mature female goat over 9 months of age Definitions and working page for page 12 177 9.0 LIVESTOCK (LIVESTOCK AND FISH) 9.1 CATTLE Number of cattle as of 1.10.2008 No. 9.1.1 9.1.2 9.1.3 9.1.4 9.1.5 9.1.6 Male calves 9.1.7 Grand total 9.1.8 What main methods do you use to identify your cattle? 9.2 Milk production: CATTLE Na. Season Type of cattle Number of milked cows (1) (2) (3) 9.2.1 Improved 9.2.2 Indigenous 9.2.3 Improved 9.2.4 Indigenous 9.3 GOAT Number of goats as of 1.10.2008 Na. 9.3.1 9.3.2 9.3.3 9.3.4 9.3.5 Grand total Milk Production: GOAT Na. Number of ilked goats (2) 9.3.6 9.3.7 (3) (4) Average of milk per goat per day (litre) Average number of days which your she goats were milked for meat Dairy (2) (3) (4) Castrated bulls (4) Did your household keep or raise cattle during 2007/08 agriculture year? Yes=1, No= 2 (If the answer is No proceed to Section 9.3) Number of indigenous cattle Type of cattle uncastrated bulls Total Number of improved cattle (5) Cows Steers (1) (2) Heifer Female calves Number of indigenous goat Tyep of goat Did your household keep or raise cattle during 2007/08 agriculture year? Yes=1, No= 2 (If the answer is No proceed to Section 9.3) Number of improved Average of milk per cow per day (litre) Average number of days which your cows were milked Dry (1) She goat Male kid She kid Season (5) Rainy Dry Rainy (3) (4) for meat Dairy Male uncastrated goat Male castrated goat Average price per litre per season (6) Average price per litre per season (5) (5) Total Cattle idenfificatio methods Iron stamp (chapa moto)…......1 Throat….2 Ear/tail cutting…..3 Colour……..4 Earings…5 Other ……………....8 Identification ● ● ● ● ● ● 178 Section 9.5 Pigs Note: Question 9.3 is for the actual number of pigs owned or raised by the household (as of 1st October 2008). This number does not include pigs kept on behalf by relatives or neighbours, that is the cattle outside the residential area of the household under survey. . 1. If the household has she goats, you would normally expect them to have kids in column Type of Sheepe (Sectioin 9.4.1 to 9.4.5) R am: Mature Uncastrated male sheept used for breeding C as trated s heep: Male sheep that has been castrated E we: Mature female sheep over 9 months of age L amb: Y oung sheep under 9 months of age. Q 9.1 and 9.3 : What is required is to establish whether or not the household kept or raised the listed livetsock during 2007/08 agricultural season (i.e. from October 2007 to September 2008). Also to establish the number of livestock as of 1st October 2008 Keeping or raising livestock is to to keep livestock at home while providing the livestock with animal feeds and medication and other services. The livestock could be owned by the farmer or kept on behalf of relatives or neighbours . Sections 9.4 Sheep Note: Q 9.4 is for the actual number of sheep owned or kept by the household (as of 1st October 2008). This number does not include sheep kept on behalf by relatives or neighbours; that is, the sheep outside the residential area of the household under survey. 1. If the the household keep ewes, it is expected that such a household will have calves which will be entered in question 9.1.6 or 9.1.7 Type of Pigs (Qs 9.5.1 to 9.5.5) Boar: Mature Uncastrated male pig used for breeing S ow: Mature female pig that has given birth to at least one ltter of pigs. G ilt; F emale pig of over 3 months up to the first farrowing P iglet: Y oung pig less than 3 months of age Definitions and working page for page 13 179 180 Definitions and working page for page 14 Control of livestock dieases causing bugs Livestock worm control medicine: Medicine used to kill or control livestock on livestock . It is often used for cattle, goats, sheep and pigs. Tiick: Is a dangerous bug that sucks blood form livestock and transmits animals diseases from one to the other animal. Tse tse fly: A fly like bug that sucks blood from livetsock and transmits diseases sleewping sickness from one to the other animal. Livestock advice (Section 9.8) IA service provided by extension officers to livestock keepers for increasing livestock production. 9.7 9.7.1 Cattle 9.7.2 Goat/Sheep 9.7.4 Poutry 9.7.5 Do you experience tick problem with your livestock? (Yes =1, No = 2, Not applicable 3) 9.7.6 How did you control tick problem? Do you experience Tse tse problem with your livestock? (Yes =1, No = 2, Not applicable 3) 9.7.8 How did you control Tse tse problem with your livestock? 9.7.9 9.7.10 How do you control Newcastle disease problem with your poutry? 9.7.11 9.7.12 How did you cotrol/ cure Fowl Typhoid with your poutry? 9.7.13 A:Foot and Mouth diseases 9.7.13B: Skin disease 9.8 Extenmsion services on livestock Na. Livestock extension advice Soure of Extension (3) 9.8.1 Feed and better feeding methods 9.8.2 Improved livestock shed (Goat, Dairy cattle, Poutry and pigs) 9.8.3 Milking and hygiene 9.8.4 Cattle fattening 9.8.5 Livetsock diseases control 9.8.6 Livestock keeping in line with land availability 9.8.7 Pasture establsihment and maintanence 9.8.8 Forming and strengthening groups/cooperatives 9.8.9 Calf rearing 9.8.10 Basics of production and use of improved bulls (AI) 9.8.11 Animals feed production 9.8.12 Other extension advice (Specify) ……………………………………… 9.7.13 Were your cattle vaccinated agaionst the following diseases? (Yes = 1, No = 2, Not applicable=3). (1) Received Extension advice (Yes=1, No=2) Did you receive the following extension advice on the followingJe? (IF THE ANSWER IS NO IN COL 2 PROCEED TO THE FOLLOWING QUESTION (2) Do you experience Newcastle disease problem with your poutry? (Yes =1, No = 2, Not applicable 3) Did you experience Fowl Typhoid with your poutry?Yes=1, No=2 , Not applicanblei=3 NOTE : If answers to Qs 9.1 to 9.6 is No (THAIS THE HOUSEHOUSE DOES NOT RAISE LIVESTOCK,) Proceed to q 9.9 LIVESTOCK DISEASES AND PEST CONTROL Which animals did your deworm? ( Yes=1,No =2, Not applicable=3 in the relevant box) Did you livestock during 2007/08 agriculture year? (Yes=1, No=2) (If the answer is No proceed to Section 9.7.5 9.7.3 Pigs Control method (Q. 9.7.6): Dipping………1 Spaying………...2 Application of medicine on back bone……..…………..3 None..4 ........... Other....…8 Control/Curative methods (Q. 9.7.10) Vaccination..1 Herbs....2 None..3 Contro/curative methods(Swali 9.7.12 Vaccination..1 Herbs....2 Noe.3 Control method (Q. 9.7.8): Dipping………1 Spaying………...2 Traps……..…………..3 None..4 ........... Other....…8 Identificatio Source of agriculture extesnion(S/wima 3) SGovernment……1 NGO/Development project.....2 Cooperative Union….3 Large Scale farmer….4 Radio/TV/Newspapere.5 Neighbour……6 Other source …..8 9.7.7 9.7.7 9.7.7 181 I Question S pecific Definitions (Q 9.9 ) Production unit number (C ol 1): A production unit is a pond river/lake which is treated as a separate entity for the production of fish eg it may be by virtue of manageable size, maturity of fish, tye of fish etc. eg. a farmer may have 3 fish ponds (each one is a separate production unit). Frequency of stocking (C ol . 5): What is the number of time the farmer puts new fingerlings into the pond each year. Fingerlings: These are young immature fish used for stocking ponds. S ols: (C ol 10 & 11) If no fish were sold enter “0” in column 10 and 11` Fish sold (Col.12) Kama hakuna samaki waliouzwa jaza "0" katika safuwima 12 General definitions Fish farming: Refers to the rearing/production of fish. It is different from fishing in that in fish farming the fish have to be reared. While in fishing, fishing nets or traps are used to catch fish from rivers, lakes and the sea; thus fishing should not be included in this section Working space for page 15 Definitions and working page for page 15 182 9.9 FISH FARMING Did your household practice fish farming? Yes=1, No=2 (If the answer is no proceed to section 9.10) Give details on the fish farming during 2007/08 agriculture year (1) (2) (6) 9.9.1 9.9.2 9.9.3 9.10 HONEY PRODUCTION Is there honey production/harvesting in your household? Yes=1, No=2 (If answer is no PROCEED to Section 9.11) Give details on honery harvesting during 2007/08 agriculture year Number 9.10.1 9.10.2 9.11 AGRICULURAL CHALLENGES Code (1) 9.11.1 Priority 1 9.11.4 Priority 4 9.11.2 Priority 2 9.11.5 Prioty 5 9.11.3 Priority 3 No. What is the main fish outlet? (7) (8) (9) (11) (12) (13) Aina ya ufugaji Square area of pond waliouzwa (kg) (m2) (3) Total number of fish harvested waliovuliwa (kg) Lulu (10) Kiwango cha Huduma ya bwawa Total weight of all fish Number of Ponds (14) Total number of stoked fish Source of fingering s What is the frequency of stocking during the period? Tialpia Mwatiko Crabs (4) (5) No With first five priorities Code (2) Number of improved bee hives Large bees Type of honey Harvesting done ? (Yes=1, No=2) Small bees (1) (2) Amount sold per year (Litre) Amount of honey sold (litre) (5) (7) (8) Main market) Price per litre Number of local bee hives (6) From the list of cahhalengs in farming on the right of the page, SELECT FIVE MAIN CHALLENGES WHICH constrain your development in agriculture LIST OF CHALLENGES (2) No Important for (1) (4) (3) mainly sold to? (Col 14) Neighbour…1 Auction……………………...3 Large Scale farmers….…..5 Open market….2 Fish processing industry..4 Private business people ….6 Did not sell…….......................……….......7 Other ….......……......8 Type of farming (SCol 2) Natural pond……….1 Small earth pond…….2 Large pond..……………….3 Other …….….………….....8 Source of fingerings(Col 4) From the pond.............................1 Neighbour……….4 Government………………..2 Business man…..5 NGO/Development Project…3 Natural Pond……..6 Other …….…………………..8 Standard of servives to the pond (Col6) High leve ………….1 Intermediate level………….2 Low leve..………3 Don't know.….……………..8 Honey outlet Co 8 Neighbour…1 Auction……………………...3 Large Scale farmers….…..5 Open market….2 Fish processing industry..4 Private business people ….6 Did not sell…….......................……….......7 01 Land availability 14 Lack of off farm incomes 02 Land owenership 15 Harvesting problems 03 Poor farm implementso 16 Kupukuchua 04 Soil fertility 17 Crop stiorage 05 Availability of imrpoved seeds 18 Crop processing 06 Irrigation services 19 Market information 07 Availability of agrochemicals 20 High transporation costs 08 Cists of farm inputs 21 Destructive animals 09 Extension services 22 Crop thefty 10 Availability of forest resources 23 Pests and diseases 11 Huntinf and collection problems 24 Advice from Local government 12 Water availability 25 Long dry spells 13 Access to credits 26 Conflicts between livetsock keepera and pastoralists Identification 2 3 1 183 Definitions and working page for page 16 10.0 Household poverty indicators Number of rooms used for sleeping in the household (Q 10.1.4) Include sitting room, during room, kitchen, etc if used for sleeping. It also includes rooms outside the main dwelling A room is defined as a space which is separate from the rest of the building by a permanent wall or division. A building / house that is not divided into rooms is considered to have one room. Household assets (Q 10.2): There assets must be functionin. Do not include if broken. Access to drinking water (Q 10.4): If there is more than one source use the one, which the hh uses most frequently. Main source of hh cash income:(Q 10.7: Activity that provides the hh with the most can during 2007/08 agricultural season. 184 10.0 POVERTY INDICATORS 10.1 HOUSE CONSTRUCTION 10.2 Household property Specify materials used in the construction of the following sehemu zifuatazo 10.1.1 Roof 10.1.2 10.1.3 Wall (1) Radio (Radio, Radio Casette, music system) Land line Celkl phone Iron Trolley Bycicle Vehicle TV/ Video Refrigerator 10.1.4 Number of bedrooms Motorbike/vespa 10.3 Energy use and availability in the hsousehold 10.4 Availability of drinking water 10.3.1 Lightining 10.3.2 Cooking 10.4.1 Rainy 10.4.2 Dry period Note: Code01, Bomba kwa Zanzibar hujulikana kama Mfereji 10.5 Toilet facilities 10.6 Eating patterns 10.5.1 What type of toilet does your hosuehold use? 10.6.1 How many meals does your hosue usually get per day ? 10.6.2 How days did the household eat meat last week? 10.6.3 How days did the household eat fish last week? 10.6.4 How many times did the household experience food shortages last year? 10.7 Main source of household cash income? 10.7.1What are the sources of household income? TIME OF FINISHING THE INTERVIEW Minutes Does your houshold woen the following?, (Yeso=1 No =2) 10.2.1 10.2.2 10.2.3 Yes=1, No=2 (Hours) Distance from source Main source of water 10.2.9 ( km) Time spent waitingor going to and from the source 10.2.4 (2) Property Number Hour (4) (3) (2) (1) Floor Season Main source of energy 10.2.6 10.2.8 10.2.7 10.2.10 10.2.5 Roofing materials Iron sheets………..1 Tiles……...……....2 Concrete…………3 Asbestos ….4 GrassiMakuti……....5 Grass and mud….6 Other ……..….. .8 Nishati za Kuangazia Umeme…………….01 Sola………...…....…02 Gesi (biogas) ………03 Taa ya kandili………04 Karabai…………..…05 Kibatari……………..06 Mishumaa…….……07 kuni……………….…08 Nyingine …………... 98 Nishati za kupikia Umeme…………….01 Sola…..................…02 Gesi (biogas) ………03 Gesi (Kiwandani)..…04 Mafuta ya taa………05 Mkaa….………….…06 Kuni …………...……07 Mabaki ya Mazao….08 Kinyesi cha Wanyama………..…09 Nyingine ……...……98 Main sourece of drinking water Col. 2 Tape water……...…..........................01 Water venders..............................09 Arificial well……..……............02 Boozer.......…10 Arificial spring... .….......…....03 Bottled water.............................11 Openwell………..….....................04 Other (Specify)............................98 Natural spring.…...................05 Lake water,piond,river,stream n etc........06 Covered Rain water harvesting well..07 O i t h ti ll 08 Food shortage problems (Swali 10.6.4) Never …………………...…1 Few times……….………….2 Sometimes…………….……..3 Many times……………….……4 Often………………..5 Code for source of income Selling food crops...........01 Sales of foerst products..05 Cash assisnatce...09 Sales of livestock....…...............02 Business.............................06 Fishingi.....................10 Sales of livestock products......03 Salaries...........................07 Other.................98 Sales of cash crops...04 Casual labour...............................08 None...................99 Tyep of toilet No toilet/in the buish…...1 Pit latrine.….4 Flash toilet……...2 Other type (Specify)………...………...8 Ordinal pit latrine..….3 Floor matrials Earthen material……………..1 Wood…...……………………….2 Wooden tiles…3 Tiles…………………………....4 Cement…………………………5 Other……………………......8 Main materials Grass and pieces of woods.….....1 Mud……...……..2 Wet bricks……….3 Burnt bricks...4 Wood……...............5 Block bricks.......6 Stonese …...………...7 Bricks /Mawe ya kichanga………….8 Idetification ● ● ● ● 185 Average/maximum yields per area Use this table to compare the yields calculated in Sections 5.1, 5.2 and 5.3. These stats are strictly to be used used as a guide for the purpose of assisting to get the correct area and yields for each crop. Name of Name of Crop Crop 11 Maize 86 Cabbage 12 Paddy 87 Tomatoes 13 Sorghum 88 Spinach 14 Bulrush Millet 89 Carrot 15 Funger Millet 90 Pepper 16 Wheat 91 Amaranthus 17 Barley 92 Pumpkin 16 Cassava 93 Cucumber 17 Sweet potatoes 94 Egg plant 18 Irish potatoes 95 Water melon 19 Yams 96 Caouliflower 25 Coco yams 52 Cotton 26 Onions 54 Coffee 27 Ginger 55 Tea 31 MaharaBeans 56 Cocoa 32 Cow peas 57 Rubber 33 Green gram 58 Wattle 34 Pigeon peas 59 Kapok 35 Chick peas 60 Sugar cane 36 Bambara nuts 61 Cardamon 41 Sun flower 71 Banana 42 Simsim 72 Avocado 43 Gound nuts 73 Mango 47 Soyabeans 74 Pawpaw 48 Caster seeds 76 Orrage 75 Pineapple 77 Grape fruit 50 Cotton 78 Grapes 51 Tobacco 79 Mandarin 53 Pyrethrum 80 Quava 62 Jute 81 Plums 44 Palm oil 82 Tufaha 45 Cononut 83 Pea 46 Cashw nut 84 Pitches 66 1,000 5,000 3,750 1,500 1,772 1,969 2,000 30,000 10,000 17,000 4,500 15,000 14,000 15,000 7,000 20,000 25,000 15,000 25,000 3,500 20,000 35,000 5,000 Kilogram/acre 57,000 35,000 20,000 27,000 40,000 50,000 30,000 40,000 150,000 40,000 100 10,000 60,000 20,000 20,000 25,000 50,000 60,000 17,000 30,000 30,000 5,000 3,000 10,000 10,000 40,000 10,000 50,000 15,000 1,000 1,400 50,000 25,000 70,000 800 500 2,500 150 400 60,000 20,243 12,146 16,194 14,170 0 8,097 10,931 23,077 0 60,729 0 20,243 0 10,121 28,340 16,194 8,097 16,194 16,194 4,049 24,291 8,097 8,097 10,121 5,668 20,243 24,291 6,883 12,146 0 16,194 16,194 4,049 24,291 6,073 12,146 2,024 6,073 2,834 0 0 6,073 324 0 24,291 1,215 4,049 0 4,049 20,243 12,146 4,049 8,097 14,170 2,024 12,146 4,049 6,883 8,097 14,170 2,024 8,097 10,121 6,073 10,121 24,291 607 607 0 1,417 2,024 3,239 24 607 607 1,619 688 405 1,619 1,012 304 709 2,024 3,441 1,822 729 2,834 4 2,530 1,619 1,417 1,215 1,012 1,822 729 2,834 3,239 121 10,121 121 202 0 324 466 607 1,012 243 202 243 243 121 243 526 121 243 304 466 567 60,000 1,500 1,500 3,500 5,000 8,000 60/tree 1,500 1,500 4,000 1,700 1,000 4,000 2,500 750 9 6,250 4,000 3,500 3,000 2,500 4,500 1,800 7,000 8,000 300 25,000 300 500 800 1,150 1,500 1,500 600 500 600 600 300 600 1,300 600 750 4,000 2,500 30,000 20,000 400 300 1,400 3,000 1,150 700 750 350 Kilogram/ha 300 1,150 121 466 466 283 304 142 Average Max Max Kilogram/acre Kilogram/ha Average Max Average Max 1,750 1,800 Average 8,500 10,000 5,000 50,000 567 1,215 30,000 1,300 1,215 243 304 3,239 3,441 1,417 Clove Black pepper Mung'unye Ocra 186 Appendix V Community Level Questionnaire Access to and Use of Community Resources Farmg Gate Prices of commodoties produced by the village Region …………………………… Ward District …………………………… Village Signature Date of Enumeration Hour Minutes Start Time End Time Field level checking by: District Supervisor Name Signature Date / / Regional Supervisor Name Signature Date / / National Supervisor Name Signature Date / / Distric checking in Office District Supervisor Name Signature Date / / For Use at Regional Level Only Data entered by: Name Signature Date / / Queried Name Signature Date / / Ministry of Agriculturte and Food Security, Ministry of Livestock and Fisheries Development, Ministry of Agriculture and Environment of Zanzibar, Ministry of Water and Irrigation, Prime Ministers' Office Regional Adminstration and Local Government, Ministry of Industry Trade and Marketing, National Bureau of Statistics, and the Office of the Government Statistician General of Revolution Governemnet of Zanzibar Enumerator Name 2007/2008 United Republic of Tanzania Village/Community Level Formats Agricultural Sample Census CONFIDENTIAL ACQ 3 NUMBER OF FARMERS HH IN THE VIALLAGE To be filled by the enumerator after completeing form ACLF2 NUMBER OF HH MEMBERS To be filled by the enumerator after completeing form ACLF2 I To be filled by the supervisor ONLY after Field/farm level checking of the enumeration process. This should be countersigned by the Supervisor in front of the enumerator All questionnaires must be checked at the district office. See the back page for details of queries y y y y m m m d d / / 187 Non G overnment Org anis ation: Is managed by people from outside the village and it normally covers more than one village/District/R egion. Its function is to provide deveoopment assistance to the farmer and is free from direct government links. Villag e level org anization: is managed by members of the village. Its purpose is normally to access/provide development assistance to the village Access to community resources. Section 1.0 Community Resources: Resources in which the hh members have no individual claim to and which are shared together by all the village Community Land: The area officiall demarcated by the village as shared/public land. Squatting farmers Land: Communal land where individual hhs make sole claim to (for crop farming or fenced livestock) without official rights to ownership. Available remaining Land: Official area of communal land minus areas of squatting farners. Givernment Land Reserve: Area set aside by the government as national reserve Community tree planting scheme(Section 14.3) C ommunity F ores t: A forest planted on the communal land which is planted, replanted or spt planted by the members of the village. P lant P lanting : An area designated by the village for planting a block of trees. S pot P lanted: R eplanting an area where selective logging has been carried out. A tree is planted to replace the one that has been cut. Indig eous Trees : T rees that are native to T anzania E xotic Trees : T rees that are not native to T anzania Definitions of some specific terms Definitions and working page for page 3 Question Specific Definitions: Obtain answers to the following questions from the meeting between the enumerator and influencial farmers in the village Infuencial people can be Village Chairman, Village Governement Executive Officer, Councillor, Ward Chairman, Extension Officer in the village or any other person in the village and who is well informed about village matters. It is important to not that these questions must be asked in groups (of more than one people) to obtain answers discussed and approved by many people. 188 ACCESS TO COMMUNAL RESOURCES 1 ACCESS TO COMMUNITY RESOURCES 1.1 Does the village set aside an area for communal resources e.g. forest, grazing, etc. (Yes =1 No =2) (If the answer is no proceed to 1.2) Are of Comminity, Village, Wrad resources 1.1.1 Total area of communal land Oficial figures from the leader 1.1.2 Area of squatting famers in communal land Key informant (Leader/Extension officer etc.) 1.1.3 Remaining available communal land Key informant (Leader/Extension officer etc.) 1.1.4 Government reserve land Key informant (Leader/Extension officer etc.) 1.2 UPATIKANAJI NA MATUMIZI YA MALIASILI ZA JUMUIYA/KIJIJI/SHEHIA Community Resources 1.2.1 Water for human consumption 1.2.2 Wtar for livestock 1.2.3 Communal grazing land 1.2.4 Communal firewood 1.2.5 Wood for chracoal burning 1.2.6 Wood for building poles 1.2.7 Forest for bee keeping (honey) 1.2.8 Hunting 1.2.9 Fishing 2.0 COMMUNITY PLANTED TREES 2.1 Didi your village have community planted trees during 2007/08 agriculture year? (Yeso=1, No=2) If the answer is no proceed to Section 3.0 Details of the community tree planting scheme No. 2.2 3.0 Non governmental Organisation (NGOs) Contact 4.0 Community Based Organisation 3.1 4.1 Visited Number of Distnatce to the Na. Type of NGO Y=1,N=2 visits Office (km) Na. Type of CBO Nd=1,Hap=2 3.2 Extension/ Rsearch 4.2 Extension/ Rsearch 3.3 Service /Input provision 4.3 Service /Input provision 3.4 Community Development 4.4 Community Development 3.5 Other 4.5 Other 5.1 5.2 5.3 5.5 5.4 Number of local ironsmiths 5.6 Did any NGO visit the village during 2007/08 agriculture year? (Yes=1,No=2) (If no provceed to Section 4) Didi the village have any CBO during the 2007/08 agricuylture year?(Yes=1, No=2) (1) Number of training centres for draft animals Did the village participate in any research on crops/ improved livestock during in the village during 2007/08 agriculture year? (Yes=1, No=2) Did the village have Field farm schools during 2007/08,agriculture year? (Yes=1,No=2) Did the village have any training centres on draft animals during 2007/08 agriculture year? (Yes=1, No=2 ) If number 2 is the answer conclude the enumeration. Did the village have local ironsmiths during 2007/08 agriculture year? (Yes=1, No=2 ) (If the answer is 2 proceed to q. 5.5 (4) Type of seeds/ Seedlings Number of (8) (7) (6) (2) (5) (4) (3) (1) (2) (3) Source of Dustance from the community forest Forest Area (acre) Type of Pllanting Trees Area in acre Distance from the resource in Km -season Main Dry Rainy Use Years since the start of planting Main uses of communal forest products agriculture year 2007/08 Main uses Msin uses (Col. 4) Home or farm /livetsock consumption...1 Sold to traders in the village...........…...2 Sold to the village market................…....3 Sold to local wholesalers........................4 Sold to Big wholewsalers .....................5 Not available.........................................6 Instructions on distance from the resource (Cols 2 and 3): Distance is estimated from the centre of the village. If under1 km 1, enter 0 If abover 1 km 1 enter whole number , eg. 1.5km= 2km, 1.25km= 1km Type of planting Col. 3) POlantion planting……….1 Spot planting…. ……...…….2 Main use of revenue (Col.8) Village development fund.1 Household use……....2 Household iIcome…. ……..3 Source of seedlings (Col. 5) Seeds collection and planting……….…..……....1 Villlage Nursery....……….…..2 Department of Forestry.………. ...….3 Private Individuals…. ……...……..4 Type of trees (Col. 4) Indigenous tress………………..1 Exotic tree….……...…….2 Both types..…………...3 Main Uses (Col. 7) Poles ……………...1 Wood ……..………..2 Charcoal ….. ……….….3 Firewoodi ………………...4 Other (Specify) 8 ● 189 Code of Minimum Maximamun Name of crop/livestock Name of main crop Main crop Per year Per year (1) (2) (3) (4) (5) (6) (7) Code of crop/livestock Price of measure Type of measure Obtain answers to the following questions from the meeting of enumerator and key informants in the village.Key infomants can be a village chairman, Village Local Government Executive Officer, Councellor, Wrad Chairman, Village extsion officer, or any knowledgeble member in the community. Where possible ask these questions to a group inorder to reach a consensus . The numebr should be below five people. Procedure: Administer this frpom after completing asll smallholder questionnaires for the village. 1. Copy the name of all crops from Sections 5.1, 5.2 and 5.3 grown in the village from smallholder questionnaires This should also include livetsock raised by the household from questions 9.1, 9.3, 9.4 and 9.5 and enter them in col na 1 of this form. Also see codes for livetsock below. 2. Enter price estimates per kg in col 5 and 6. Main poroduct- CROPS (sCol.4) Cereals…………...............01 Flowers eg. Pyrethrum.....07 Green maize…................02 Vegetables….......,08 Green leaves and stem ........03 Fruit…………….....09 Straw, dry stems etc..04 Other………….....10 Roots and tubers, etc......05 Leaves (Tobacco etc)...... …..06 Main product- LIVESTOCK (Col. 4) Live animals…..01 Meat ...........02 Milk...........03 Eggs.............04 Hid d ki 05 Type of livestock(Col 2) Cattle ......01 Ducks………………..07 Goat...........02 Turkey……….08 Sheep.........03 Rabbit……………09 Pigs......04 Kanga………………10 Poutry………..05 Simbilisi………….….11 Donkeys………06 Q uantity (Col.5) Kg…….1 Number.......2 Litre……..3 A portion/piece ..4 190 Appendix V Village Community Level formats CONFIDENTIAL ACLF 1 Page Number………….. out of……………… Sub-village /ward leader listing from Comments (3) (5) (1) (2) (4) District _____________________Code Village ________________________ Code Sub village leader Number Name of Ward village leader Number of Households Form Office Register After enumeration UNITED REPUBLIC OF TANZANIA Agriculture Sample Census 2007/08 Region ______________________Code Ward _______________________Code 191 ACLF 2 Page Number………….. out of……………… Household listing from-for listing hh heads and agriculture activities Region Code District Code Name of sub village leader Ward Code Name of sub village___________________________________________ Village Code (1) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (2) Total Bulls Cows Calves Sheep Pigs Kuku/Bata/ Rabbit UNITED REPUBLIC OF TANZANIA Agriculture Sample Census 2007/08 Household number Household head name Number of If the Respondent Qualifies X Farmer Serial Number Fields a Cattle Goats CONFIDENTIAL 192 ACLF 3 Region Code ward : code Namba Sawia District village code Hatua Code (1) (5) (6) (7) (8) (9) (10) (11) Poutry (2) (3) (4) Cattle Goat Sheep Pigs UNITED REPUBLIC OF TANZANIA National Agriculture Sample Census 2007/08 Household listing for 15 selected farmers S/N Sub-village leader Number Name of sub-village leader Name of selected head of household Name of a Househol d Head Number of Field CONFIDENTIAL

# Extracted Content 1 THE CEREALS AND OTHER PRODUCE REGULATIONS, 2011 ARRANGEMENT OF REGULATIONS PART I PRELIMINARY PROVISIONS Regulation Title 1. Citation. 2. Application. 3. Interpretation. PART II CRITERIA FOR PERFOMANCE OF COMMERCIAL FUNCTIONS OF THE BOARD 4. Specified crops. 5. Zonal branches and appointment of branch Managers. 6. Obligations of zonal branch Managers to submit report. PART III PROMOTION OF SPECIFIED CROPS 7. Promotional functions. 8. Regulation on production. 9. Good crop husbandry. 10. Hygiene and quality matters. PART IV QUALITY ASSURANCE, GRADING AND WEIGHING 11. Quality control and safety. 12. Grading of specific crops. 13. Weighing equipments to be inspected and approved. 14. Notification for weighing of specified crops. 2 PART V MARKETING AND SALES PROCEDURES 15. Performance of commercial functions. 16. Direction of the Minister. 17. Negotiations and setting initial price. PART VI ZONAL COUNCIL FORUM 18. Composition of zonal council. 19. Tenure of zonal council membership. 20. Cessation of membership. 21. Performance of zonal council functions. 22. Recommendation of council representatives. 23. Procedures for convening the meeting. 24. Zonal council secretariat. 25. Disciplinary breach by members. 26. Disciplinary proceedings. 27. Zonal Council working group. PART VII ADMINISTRATIVE MATTERS 28. Guidelines. 29. Employees of the Board. 30. Terms of service. 31. Staff performance evaluation scheme. 32. Clients service charter. PART IX MISCELLANEOUS 33. Information and data collection. 34. Contracts entered into by the Board. 35. Adherence to relevant laws. SCHEDULES 3 GOVERNMENT NOTICE NO. …………...................... published on. ………………….. CEREALS AND OTHER PRODUCE ACT (CAP 274) __________ REGULATIONS __________ (Made under section 26) __________ CEREALS AND OTHER PRODUCE REGULATIONS, 2011 PART I PRELIMINARY PROVISIONS Citation 1. These Regulations may be cited as the Cereals and Other Produce Regulations, 2011. Application 2. These Regulations shall apply to any Specified crops grown and processed in Tanzania Mainland as well as imported raw or processed Specified cereals and other produce which the Board is dealing with. Interpretation 3. In these Regulations unless the context otherwise requires- Cap. 274 “Act” means the Cereals and Other Produce Act; Cap. 337 “Association” means an association formed and registered under the Societies Act; “Authorized officer” means an officer appointed by the Director General or the Board to act on behalf of the Board; “Board” means the Cereals and Other Produce Board of Tanzania established under section 4 of the Act; “Branch” means the Zonal office of the Board established under regulation 5; Cap. 211 “Cooperative Society” means the Cooperative Society registered under the Cooperatives Societies Act; “Council” has the meaning ascribed to it under the Act. “Council forum” means a meeting of zonal Cereals and Other Produce stakeholders referred to under Part VI; 4 “Director” means the Director responsible for Crop Development in the Ministry; “grower” means an individual grower, association, cooperative society, company or any other entity producing Specified crops; “initial price” means minimum price offered by the Board after consultation with Specified crops stakeholders in which specified cereals or any other produce will be bought; “Minister” means the Minister responsible for Agriculture; “other Produce” has the meaning ascribed to it under the Act; “specified crop” means agricultural crops specified under these Regulations. PART II CRITERIA FOR PERFOMANCE OF THE COMMERCIAL FUNCTIONS OF THE BOARD Specified crops 4.-(1) The Board shall, in executing its commercial functions, be limited to crops specified by the Minister in the First Schedule. (2) The Minister may, by order published in the Gazette, amend, vary or replace all or any of the provisions of the First Schedule. (3) The Minister shall, for the purpose of specifying crops under sub regulation (2) , consider the following criteria- (a) standards and specification for regulating the specified crops are in place; (b) production of specified crops meet Commercial Viability Test; (c) written application for the addition of specified crops in the First schedule. (4) An application under paragraph (c) of sub regulation (3) shall be made by the Council and submitted to the Minister for consideration. (5) For the purpose of these Regulations, Commercial Viability Test shall include- (a) evidence that the crop can be commercially traded or that there is market for that particular crop, both locally and internationally; (b) necessary infrastructure including, roads, storage and warehousing is available; and (c) Board has a technical and financial capacity to handle the trade. (6) The Minister shall, within sixty days from the date of receipt of the written application in accordance with the provisions of sub regulation 1(c), notify the Council concerned of its decision. 5 Zonal branches and appointment of branch managers 5.-(1) The Board may, for the purpose of facilitating performance of its functions, establish branches in the zones to which it shall assign its commercial functions as provided for in the Act. (2) The Board may appoint qualified persons to be zonal branch managers. Obligation of Zonal branch managers to submit report. 6. Branch Managers shall attend zonal council meetings as members representing the Board and shall report the proceedings to the Director General of the Board. PART III PROMOTION OF SPECIFIED CROPS Promotional functions 7.-(1) The Board shall participate in the implementation of shared functions as agreed by stakeholders at the Council meetings. (2) The Board shall in the performance of its commercial functions take every initiative to promote contract farming and safeguard the interest of growers. Regulation on production 8. The Board may, through its branches, enter into agreement with any Cooperative Society, Association, Company or individual grower in relation to- (a) farming, purchase, distribution, storage, processing, grading, packaging and marketing of any specified crop; (b) facilitation of extension service; and (c) facilitation of good crop husbandry. Good crop husbandry 9. The Board may recommend good agricultural practices of specified crops to growers in a specific agro-ecological zone. Hygiene and quality matters 10. The Board shall ensure that any premise used for buying, selling, transportation or storage of Cereals and Other Produce complies with photo sanitary, hygienic and safety standards as provided by relevant authorities. PART IV QUALITY ASSURANCE, GRADING AND WEIGHING Quality control and safety 11.-(1) The Board shall buy crops specified in the First Schedule to these Regulations. 6 (2) The Board shall, in performing its functions, observe safety and quality requirements as prescribed by the relevant authority. Grading of specified crops 12.-(1) The specification for grading of each specified crop shall be in accordance with the approved national standards. (2) If no national standards of grading is provided in the country, specified crop grading shall be based on international specified crop grading system or any other standard approved by the Minister. Weighing equipments to be inspected and approved Cap 340 13. The weighing scales and measures to be used by the Board for purchase or sale shall be inspected and approved in accordance with the Weights and Measures Act. Notification for weighing of specified crops. 14. The Board shall notify the grower of the time and place the weighing is due to take place and the grower may, either personally or by representation, be present at the weighing point. PART V MARKETING AND SALES PROCEDURES Performance of commercial functions 15. The Board shall, in performing its commercial functions, safeguard the interest of growers by ensuring that the growers sell their produce at competitive price and are assured of market. Direction of the Minister 16. The Directions given by the Minister under Section 21 of the Act may include- (a) matters which are taken into account in determining the price of any specified crop to be purchased or sold by the Board; (b) manner in which price may be computed; and (c) installments by which price shall be paid. Negotiations and setting of initial price 17.-(1) The Board may, after consultation with other stakeholders, set its initial price for buying specified crop for each year which shall be the minimal price in the purchase of specified crop. (2) The initial price referred to under sub regulation (1) shall be determined by the Board based on the prevailing market price. (3) The parties to a contract farming arrangement may negotiate for a price of specified crops above the minimal price. 7 PART VI ZONAL COUNCIL FORUM Composition of zonal council 18.-(1) The zonal councils established under section 15 of the Act shall be composed of representatives specified under the Second Schedule. (2) A person shall not be eligible for selection under sub-regulation (1) unless he is conversant with the specified crops of a particular zone. (3) The members of each zonal council shall have the right to vote at the zonal council meeting. Tenure of zonal council membership 19. A member of the zonal council shall hold office for a term of three years from the date of appointment and may be eligible for re- appointment for a further term of three years. cessation of membership 20. A person shall cease to be a zonal council member if he- (a) resigns; (b) dies; (c) is convicted of an offence; (d) is of unsound mind; and (e) no longer lives in the zone. Performance of zonal council functions 21. In performing its functions provided under section 15(3) of the Act, the zonal council shall- (a) deliberate on funding mechanism of shared functions; (b) establish its organs for the better carrying out of shared functions; (c) recommend names of zonal council representatives to be appointed by the Board; (d) discuss and approve development plans of specified crops; (e) determine modalities for financing its meetings and activities; and (f) implement any other matter for sustainability and stability of the specified crop. Recommendati on of council representatives 22.-(1) A Zonal council shall, by order of priority, recommend three names of persons to be council representative of which one person shall be appointed by the Minister to represent each agricultural zone in the Board. (2) Three names of the persons recommended by the council under 8 sub-regulation (1) shall be from different clusters of stakeholders provided that one shall be a grower. Procedures for convening the meeting 23 Without prejudice to the provisions of Section 16 of the Act, Zonal Councils may adopt a model of general meeting procedures as prescribed in the Second Schedule to these Regulations. Zonal council secretariat 24. A zonal council shall establish a secretariat which shall- (a) co-ordinate the implementation of all zonal council resolutions; (b) prepare and coordinate meetings of the council; (c) advise the council on technical matters; (d) provide progress report on the implementation of zonal council resolutions; (e) prepare and submit zonal council reports to zonal council members; (f) establish and maintain a data bank of records and information of the zone; (g) establish sources of revenue for the operation of zonal council activities; and (h) perform any other matter as the zonal council may direct. Disciplinary breach by members 25. Any member of a zonal council who- (a) does not declare conflict of interest; (b) without authority, conducts himself as an official spokesperson on behalf of the stakeholders; (c) utters any false information; (d) discloses information of the council without authorization; (e) behaves in an unethical manner or conduct: commits a disciplinary offence and shall be subjected to a disciplinary action in accordance with regulation 26. Disciplinary proceedings 26.-(1) A zonal council member may submit to his chairperson a report of any disciplinary offence committed under regulation 25 (2) The chair person shall, upon receipt of a report under sub regulation (1), conduct a disciplinary proceeding in accordance with the procedures prescribed in the Second Schedule. (3) A person aggrieved by a disciplinary action may, within thirty days from the date of the decision, lodge an appeal to the Minister. (4) The Minister shall, within thirty days from receipt of appeal by an aggrieved person, make a decision in writing and serve a copy to the 9 aggrieved person. (5) A decision made by the Minister under sub-regulation (4) shall be final. Zonal Council working group 27.-(1) The zonal council meeting may form a working group as may deem necessary to perform specified functions. (2) A working group established under sub-regulation (1) shall perform its activities under the terms of references provided to it by the zonal council. PART VII ADMINISTRATIVE MATTERS Guidelines 28. The Board may issue administrative guidelines for better carrying out of its functions. Employees of the Board Cap 298 29. An employee of the Board shall perform his functions based on the terms and conditions provided under the Public Service Act. Terms of service 30. Terms of service of employees of the Board shall be as per prevailing staff regulations or employment contract. Staff performance evaluation scheme 31. The Board shall develop staff performance evaluation scheme which shall base on the Cereals and Other Produce Board of Tanzania strategic plan. Clients service charter 32. The Board shall establish a client’s service charter to govern its staff. PART VIII MISCELLANEOUS Information and data collection 33.-(1) The Board may collect, keep and maintain all statistical data and information relating to its functions. (2) The data and information obtained under sub-regulation (1) may be accessed by public. contracts entered into by the Board 34. No contract entered into between the Board and any other person shall, unless otherwise expressed in the terms of that contract, be void or unenforceable by reason only that performance of such contract or any term of such contract would be in contravention of any direction made under regulation 16. 10 Provided that- (a) such contract was made before the date on which the direction is issued. (b) the provision of this regulation shall cease to apply to any such contract upon the expiration of twelve (12) months from the date of such direction, and the expiration conferred by this regulation shall thereupon terminate. Adherence to relevant laws 35. Without prejudice to any provisions of the Act and these Regulations, the Board shall perform its functions in accordance with the laws of the Country. 11 ____________ FIRST SCHEDULE ____________ SPECIFIED CROPS ________ (Made under Regulation 4) ________ 1. Maize 2. Paddy/Rice 12 ____________ SECOND SCHEDULE _________ Made under Regulation 23 _________ PROCEDURES FOR ZONAL COUNCIL MEETING 1.0 Purpose The first Council’ meeting shall establish meeting procedures for operationalisation of council and proper conduct of its meeting. According to Section 16 of the Act the Council shall have power to regulate its own procedures in respect of the meetings and proper conduct of its business. The following schedule describes the modal procedures for the conduct of zonal council meeting which may be adopted by zonal council. 2.0 Structure and Membership 2.1 Composition For the purpose of maintaining balance amongst stakeholder clusters, each Zonal Council meeting shall compose of representatives from each district or region growing specified crop and organizations with interest in Cereals and Other Produce Industry. The cluster representation shall be as follows and may be amended from time to time as agreed by stakeholders- (1) Two Growers from every Local Authority. (2) Two Traders from each Region. (3) Two Input suppliers from every Region. (4) Two Processors from every Region. (5) Zonal Research and Extension liaison officer. (6) Two Financial Institutions operating in each Zone. (7) A Regional Authority representative. (8) An Executive Director of each Local Authority. (9) Any other representatives as agreed by the stakeholders. 13 3.0 Stakeholders’ meetings 3.1 The Council secretariat shall schedule zonal councils’ meetings on annual basis, or on an “as-needed” basis when requested by the Stakeholders of that specific zone. 3.2 The Council secretariat shall serve as facilitator for all zonal Council meetings. 3.3 Stakeholder Working Group meetings shall be scheduled on an “as-needed” basis by stakeholders. 3.4 Notice of each Stakeholder or Working Group meeting will be posted or advertised on the Newspaper of wide circulation. E-mail notifications shall be sent by the Council secretariat to the designated contact or signatory specified as well as to all other subscribers to the Stakeholder Process e-mail list. Meeting date, time, location, and draft agenda information shall be made available at least two weeks prior to each meeting. 3.5 Solicitation for meeting agenda items shall be included in each meeting announcement. Final meeting agenda and associated meeting materials shall be posted before the meeting. 3.6 Draft minutes of each Council meeting or Working Group meeting shall be made available through postings of each stakeholder. Notice of the posting of draft meeting minutes will be sent to all Stakeholder addresses. 3.7 Final minutes of each Stakeholder’s meeting shall be adopted at the subsequent meeting. 3.8 Half of the members present shall constitute a Quorum of the meeting. 4.0 Chairperson of the meeting 4.1 The Council meeting as a whole shall appoint a Council meeting Chairperson. The Chairperson shall serve not more than two consecutive terms of a maximum of two (2) years per term. The Chairperson should step off for one (1) term after final term before having the ability to be nominated again. 4.2 The Chairperson shall chair all meetings. If the Chairperson is not present thirty (30) minutes after the time set for the meeting, or it is known that he/she will not be able to attend, the meeting can still be held if the Members present comprise a quorum and appoint an interim Chairperson to preside over that only meeting. If items or circumstances that are not covered in these Regulations and Procedures should arise at a meeting, then the Chairperson shall decide on the course of action. 14 4.3 The Chairperson shall endeavor to achieve a full discussion by the Council meeting of all agenda items and employ his/her best effort to allow all district and regional representatives an adequate voice during the meetings. The Secretariat shall be responsible for recording meeting notes and drafting recommendations. 5.0 Attendance and Representation 5.1 All Council meeting members are expected to attend all required meetings of the Stakeholders. Members who are unavoidably absent should send in their written views preferably before the meeting so that her/his views can be made known, recorded and taken into account by those present. All written views received sufficiently in advance of the meeting shall be taken into account in the written recommendations submitted to the Secretariat. 6.0 Stakeholders Membership - Representation 6.1 Council meeting membership vested in the individual is expected to represent broadly the stakeholders’ clusters from which they were elected or appointed. 6.2 If any Council meeting member changes position to a different constituent cluster it will remain up to stakeholder who he/she represents to determine if she/he can effectively represent his/her former stakeholder cluster. If not, then the member will be expected to submit a written letter of resignation to the Council meeting Chairperson. Such vacancies will need to be filled by the cluster responsible. 7.0 Operations 7.1 Meetings 7.1.1 Scheduling Meetings Regular meetings of the Council will be held at minimum once a year. The Secretariat, through the Chairperson, will propose locations and dates for the meetings to the council meeting one year in advance. 7.1.2 Adequate Notice of Meetings The date and location of annual meetings will be determined at the previous annual meeting, giving all members one year’s advance notice. Should the date or venue of the annual meeting need to be changed for logistical reasons, all members will be notified of the change at least thirty (30) days in advance. Draft agendas and proposed resolutions 15 will be circulated at least fourteen (14) days in advance of the annual meeting in order to assure that all members have time to review and respond to them before the meeting. 8.0 Establishing Meeting Agendas 8.1 The Chairperson, in consultation with the Secretariat, will propose an agenda. Any member may propose an agenda item. This must be provided in writing to the Chairperson with a copy to the Secretariat. The final agenda and meeting papers may be circulated five (5) days in advance of the meeting. 8.2 When formulating the meeting agenda, the Chairperson and Secretariat shall take into consideration the need for adequate time for a thorough discussion of all agenda items. 9.0 Proposing and Passing Resolutions 9.1 When a resolution is to be determined by a vote, a simple majority of the members is required to pass the resolution. 9.2 When the vote concerns a matter of principle, the vote shall be a show of hands or by open ballot and the votes recorded. 9.3 When voting concerns a matter of a person or persons (e.g., appointment), such vote must be by secret ballot. If there is no challenge to the ballot results by the time the meeting terminates the ballot papers shall be destroyed. 9.4 When a member casts a vote, he/she may state reasons and such reasons shall be noted. Members may also wish to abstain from a vote. 9.5 Resolutions of the meeting of the stakeholders’ meeting may also be adopted in a manner other than at a meeting, in writing or otherwise, provided the proposal concerned is submitted to all members and none of them objects to the relevant manner of adopting resolutions. 9.6 As much as possible, simple and clear language should be used in the wording of resolutions. 10.0 Conflict of Interest During deliberations any stakeholder with conflict of interest must declare his/her interest before deliberation. Signed statement must be submitted annually. This statement is intended to supplement, but not replace, any laws governing conflict of interest. At any time, if a member realizes that s/he has or may have a position of conflict; s/he must 16 immediately bring this to the attention of the Chairperson who will then decide on the appropriate course of action. If there is any doubt about conflict, it is strongly advised that members consult the Chairperson. 11.0 Extraordinary Meetings The Chairperson may request an extraordinary general meeting to consider issues of significant importance. An extraordinary general meeting should only be called if the matter cannot wait to be considered at the regular annual meeting. An extraordinary general meeting can be held in person (given that budget allows) or through electronic communication. Decisions will be made in the same manner as at regular meetings. 12.0 Relations with other Governance Bodies 12.1 General Communication Notice shall be given when actions are taken by any of the governance bodies mentioned in this section that affect any other body. Communications should occur between Chairperson of bodies with the secretariat liaison person acting as an intermediary as needed. 12.2 Relations with the Board of Directors 12.2.1 To demonstrate that it has discharged its stewardship properly the Secretariat must present the annual report of coordinating the Stakeholders activities to the Board and to the Council meeting at its annual general meeting. 12.2.2 The Board Chairperson can invite the Council meeting Chairperson to attend Board meetings, as needed. S/he will not be able to vote, and will be allowed to speak at the discretion of the Board Chairperson. 13.0 External Relations 13.1 Official Working Language The official working languages of the Stakeholders meeting are Swahili and English. All communications will be prepared either in English or Swahili or in both. 14.0 Role of Council Members Externally 14.1 Designated Spokespersons 17 The Council meeting Chairperson is the designated spokesperson for the Stakeholders meeting. S/he may provide official input on council meeting matters with media, Government and other stakeholders requiring formal input. Council meeting members may speak publicly as individuals participating in the Stakeholder meeting, but are not official spokespersons on behalf of the Stakeholders meeting. 14.2 Interactions with Stakeholders Council meeting members are free to speak with the media, government, and other stakeholders about Council meeting matters but must clearly explain that s/he does not speak on behalf of the Stakeholders’ meeting in any official capacity. 15.0 Description of duties and responsibilities for Chairperson 1. Consult with the Secretariat in preparation of agenda for council meetings. 2. Chairperson of council meetings. 3. Be a spokesperson for the council meeting with media, Government and other stakeholder groups seeking public statements. 4. Ensure that all members are enabled and encouraged to participate fully and collectively and are involved in the role and purpose of the council meeting. 5. Ensure that Council meeting members receive timely relevant information and that they are briefed properly on agenda items and other issues that may arise at the meetings. 6. Remain objective in the implementation of one’s duties and to avoid partisanship based upon the district and region from which one originates. 7. Ensure business of the meeting is within the budget set for the meeting. Dar es Salaam, JUMANNE A. MAGHEMBE ………………, 2011 Minister for Agriculture, Food Security and Cooperatives

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# Extracted Content 1 THE SISAL INDUSTRY REGULATIONS, 2011 ARRANGEMENT OF REGULATIONS PART I PRELIMINARY PROVISIONS Regulation Title 1. Citation. 2. Application 3. Interpretation. PART II SISAL CULTIVATION AND HUSBANDRY 4. Sisal crop husbandry. 5. Specification of sisal. 6. Sources of planting materials. 7. Restrictions on planting materials. 8. Quarantine matters. 9. Control of pests and diseases. 10. Environmental protection. 11. Sanitary measures. 12. Provisions of data and information. PART III REGISTRATION 13. Application for registration and issue of registration certificate. 14. Assignment of powers of registration. 15. De-registration. 16. Board to keep and maintain separate registers. 17. Data and information. PART IV LICENSING 18. Categories of licenses issued by the Board. 19. Mode of application for a license. 20. Refusal to issue license and appeal. 21. Powers to revoke or suspend license. 22. Appeal for revocation and suspension. 23. Transfer and assignment of license. PART V QUALITY ASSURANCE AND INSPECTION 24. Appointment of quality assurance officers. 2 25. Functions of quality assurance officers. 26. Powers of quality assurance officers. 27. Grading, blending or marking of sisal and sisal products. 28. Restriction on use of grades or blends. PART VI CONTRACT FARMING 29. Contract farming. 30. Contents of contract of farming. 31. Registration of contract of farming. 32. Failure to register a contract farming agreement 33. Dispute settlement mechanism. 34. Review of standard form contract. PART VII SALES AND MARKETING 35. Board to announce indicative price. PART VIII STAKEHOLDERS FORUM 36. Composition of annual stakeholders meeting. 37. Role of stakeholders meeting. 38. Procedures for stakeholders meetings. 39. Implementation of stakeholders meeting resolution. PART IX SHARED FUNCTIONS BY LOCAL GOVERNMENT AUTHORITIES 40. Matters for consideration by Local Government Authorities. 41. Implementation of stakeholders meeting resolutions by Local government authorities. PART XI MISCELLANEOUS PROVISIONS 42. Strategic plan. 43. Power to issue guidelines. 44. Board to develop staff performance standards. 45. Value chain analysis in service delivery. 46 Information and data collection. 47. General offence and penalty. 48. Corporate liability. 49. Compliance with the Act. _______ SCHEDULES ______ 3 GOVERNMENT NOTICE NO………………published on……. THE SISAL INDUSTRY ACT (CAP. 30) _________ (Made under Section 20) ___________ SISAL INDUSTRY REGULATIONS, 2011 PART I PRELIMINARY PROVISIONS Citation 1. These Regulations may be cited as the Sisal Industry Regulations, 2011. Application 2. These Regulations shall apply to any type or grade of sisal grown and sisal products produced, imported into or exported from Mainland Tanzania. Interpretation Cap. 30 Cap. 337 3. In these Regulations, unless the context requires otherwise: “Act” means the Sisal Industry Act,; “Association’’ means an association formed and registered under the Societies Act; “Authorized Officer” means any officer acting on behalf of the Board; “Board” means the Tanzania Sisal Board established under section 3 of the Act; “bulbil” means a sisal plantlet, which arises from a tiny bud on the pole of a sisal plant. It is also a form of vegetative reproduction of the plant which can be used as a planting material; “contract farming” means farming under the agreement between growers on one part and financiers including sisal buyers, factory owners and investors and bankers on the other part; “Director” means a director responsible for crop development; “Director General” means the chief executive officer of the Board appointed under the Act; “Exporter” means a person registered and licensed by the Board to 4 export sisal or sisal products; “grower” means a person, association, company or cooperative society registered and licensed by the Board to grow sisal; “Minister” means the Minister responsible for agriculture; “processor’’ means a person or company registered and licensed by the Board to process sisal; “manufacturer” means a person or company registered and licensed by the Board to process sisal to manufacture ropes, sisal yarns, twines, carpets, geo textiles and other products. “trader’’ means a person , company or association registered and licensed by the Board to buy, sell, import or export sisal or sisal products; “Quality assurance officer” means an officer appointed by the Board to oversee the quality of sisal or sisal products and other matters related thereto; “sisal” means the plant agave sisalana or other species of the genus agave, or of the hybrids of any such species and fibre thereof, and includes brush tow, flume tow, twines, ropes, yarns, or any other products manufactured which contains predominantly fibre or any of its derivatives; “sisal designated land” means a land which is held under a Government lease or right of occupancy, the title of which is for sisal growing; Cap. 211 “society” means a cooperative society registered under the Cooperative Societies Act. PART II SISAL CULTIVATION AND HUSBANDRY Sisal crop husbandry 4.-(1) A grower shall only grow sisal varieties approved by the Director. (2) A grower shall grow sisal in accordance with the recommended practices prescribed in the First Schedule to these Regulations. Crop specification 5.-(1) The Board shall ensure that a grower adheres to crop specifications prescribed by the Director. (2) The crop specifications under sub-regulation (1) shall include the use of agro chemicals such as fertilizers, herbicides, fungicides, nematicides and any other materials related to sisal growing. (3) The chemicals mentioned under sub regulation (2) shall be those which have been tested and approved by a competent sisal research institute 5 Sources of planting materials 6. A grower shall develop and maintain nurseries as source of planting materials and shall, on exceptional cases authorized by the Board, use suckers properly selected and graded. Restriction on importation of planting materials Cap. 133 7. - (1) A person shall import sisal planting materials after obtaining a permit in accordance with relevant law on plant protection. (2) Any person who contravenes the provision of sub regulation (1) commits an offence and shall upon conviction be liable to a fine or imprisonment in accordance with the Plant Protection Act. Quarantine matters Cap. 133 8.-(1) Subject to the relevant law on plant protection, the Director or any other authorized officer may by order under his hand and for purposes of preventing, controlling occurrence or spread of any pests or diseases of sisal within any area specified in that order- (a) prohibit or regulate the planting of sisal seedling or any other crop specified in the order which is known to harbor pests or disease; (b) prohibit the removal from any sisal growing areas, sisal plants or any article which in the opinion of the Director is likely to harbor pests or disease of sisal plants; (c) require the uprooting and burning of all or any sisal plants or any plant specified in the order by a person processing or having control over that sisal plant ; or (d) prohibit any person from planting sisal for the period specified in the order. (2) Any person who fails to comply with an order issued under sub-regulation (1) commits an offence. (3) The Director or an authorized officer, may, within seven (7) days upon issuance of an order under sub regulation (1), cause the removal or destruction of an area likely to cause the spread pests and diseases (4) Without prejudice to any penalty imposed under sub regulation (1), any costs incurred as a result of the removal or destruction caused by the Director shall be recoverable as a debt due to the Government. Control of pests and diseases Cap. 113 9. For the purposes of control of pests and diseases, growers shall comply with the following - (a) compulsory control measures for the following pests - (i) sisal weevil (scyphosphorus interstitialis); (ii) scale insects which are species in the general aonidiella orientalis and aonidiella andersoni, aspidiotus and lepidosaphes; and (iii) vermin including animals such as elephants, baboons, wild pigs, monkeys, mole rats 6 (heliophobius spalax), porcupines (hystrix galeata) and snails (gastropoda sp.); (b) compulsory control measures for the following diseases- (i) bole rot caused by a black-spored fungus known as aspergillus niger; (ii) zebra disease caused by the fungus known as phytophthora; and (iii) ring spot disease (anthracnose) caused by fungus known as colletotrichum agaves. Environmental protection 10.-(1) A grower shall, in order to conserve the environment- (a) not dump any sisal garbage, residue and other refuse in the sisal field or in water bodies; (b) transport, use or store agrochemicals in an appropriate manner so as not to pose danger to environment; (c) use appropriate farming practices that will ensure environmental protection. (2) A processor or manufacturer shall construct a dumping ground in an appropriate place for the purpose of dumping of garbage, residue or any other refuse likely to pose danger to environmental sanitation. Sanitary measures 11.- (1) A processor or manufacturer shall undertake all precautionary measures in ensuring that his produce is properly stored and is free from foreseeable hazards. (2) Any person being an owner or having control of premises for processing or storage of sisal or sisal products shall not store or allow materials, articles or chemicals in such premises which are hazardous to life, or which may destroy or distort the quality or standards of sisal or sisal products. (3) A person who transports sisal or sisal products shall adhere to clean and safety standards of transportation prescribed by the Board. Provision of data and information 12. A grower shall, in each year, provide to the Board data and information in respect of sisal cultivation, husbandry and appropriate returns in a form prescribed under the Second Schedule. PART III REGISTRATION Application for registration 13-(1) Any person being a grower, processor, manufacturer 7 and issuance of registration certificate. or trader in respect of sisal or sisal products shall make an application for registration to the Board in the prescribed form under the Third Schedule. (2) Without prejudice to the generality of sub regulation (1), an application for registration shall contain the following information- (a) full name and address of the applicant; (b) type of activity or business; (c) location and description of the land area, factory premises or business premises where sisal is grown, processed or traded; (d) ownership of the entity; (e) projected business plans and expansion programs; (f) organization and manpower structure; (g) any other information, which may be deemed necessary for the purpose of registration; (3) The Board shall upon application, register and issue a registration certificate and a registration number to any applicant in fulfillment of the terms and conditions for registration. (4) A certificate for registration issued pursuant to sub regulation (3) shall be in the form prescribed in the Third Schedule. (5) A registered grower, processor, manufacturer or trader shall at all times quote his registration number when corresponding with the Board. (6) A person shall not grow, process, manufacture or trade in sisal or sisal products unless he is registered by the Board. (7) A person who fails to comply with the provisions of sub regulation (6) commits an offence. Assignment of powers of registration 14.-(1) The Board may appoint an agent to perform the power of registration on such terms and conditions as it may specify. (2) An agent appointed under sub regulation (1) shall perform the registration activities specified by the Board in conformity to the provisions of these Regulations. De-registration 15.-(1) The Board may deregister a grower, processor, manufacturer or trader where it is satisfied that he - (a) is no longer carrying out the sisal business; (b) without reasonable cause, does not comply with the provisions of the Act or these Regulations; (c) fails to file or provide returns to the Board as required under regulation 17 ; or voluntarily withdraws his registration by giving a three months written notice to the Board. (2) A person aggrieved by the decision of the Board made pursuant to sub regulation (1) may, within sixty days from the date of 8 receipt of the decision, appeal to the Minister. (3) A person who was registered and subsequently deregistered by the Board may be re-registered upon such additional terms and conditions as the Board may specify. Board to keep and maintain separate registers 16. The Board shall keep and maintain a separate register for growers, processors, manufacturers and traders. Data and information 17. A registered grower, processor, manufacturer or trader shall submit returns to the Board and any other information as the Board may require using forms prescribed in the Second and Seventh Schedules to these Regulations. PART IV LICENSING Categories of licenses issued by the Board 18.- (1) The Board shall upon application and on such terms and conditions provided under these Regulations, issue the following categories of licenses to a person registered under these Regulations- (a) sisal processing license; (b) sisal manufacturing license; (c) sisal export license; (d) sisal import license; and (e) sisal trading license; (2) All licenses issued under sub regulation (1) shall be in a prescribed form provided under the Fifth Schedule. (3) A licence shall be valid for a period of one year and may be renewed for another period of one year Mode of application for a license 19. (1) A grower, processor, manufacturer or trader shall apply to the Board for a license using an application form prescribed in the Fourth Schedule to these Regulations. (2) For the purpose of application for a license, an applicant shall submit to the Board the following information- (a) evidence of registration by the Board: and (b) any other information as may be deemed necessary for the purpose of licensing. (3) Where the Board is satisfied that the applicant has satisfied all requirements necessary for the issuance of the license, it shall within fourteen days from the date of receipt of the application issue the license. (4) Any license issued by the Board under these Regulations shall be issued subject to terms and conditions as the 9 Board may impose. Refusal to issue license and appeal 20.-(1) The Board may refuse to issue a license where it is satisfied that- (a) the applicant has failed to fulfill the requirements necessary for the issuance of the license ; (b) the applicant has no sufficient knowledge, facilities, experience, or financial resources to carry out properly the business of processing, manufacturing, trading, importing or exporting of sisal or sisal products; (c) the applicant is unable to comply with the provisions of these Regulations relating to carrying out of the business for which he has applied for. (2) A person aggrieved by the decision of the Board pursuant to the provisions of this regulation may appeal to the Minister within sixty days after the date of the receipt of the decision. Powers to revoke or suspend license 21.-(1) The Board may revoke or suspend a license if the holder of the license has- (a) been convicted of an offence under the Act and these Regulations; (b) ceased to carry out the business in respect of which the license was issued; (2) The Board shall give reasons and accord an opportunity to be heard to any person whose license has been revoked or suspended. (3) A suspension period of a license made under this Regulation shall not exceed sixty days. (4) Any person whose license has been revoked or suspended by the Board shall not transact in any business for which a license is issued during the period of revocation or suspension. (5) Any person who fails to comply with the provisions of sub regulation (4) commits an offence. (6) The Board may lift revocation or suspension of a license where the circumstance under which the license was revoked or suspended has been rectified. Appeal for revocation and suspension 22. A person aggrieved by the decision of the Board in respect of the revocation or suspension of a license under these Regulations, may, within sixty days, appeal in writing to the Minister. Transfer and assignment of license 23.-(1) A person issued with a license by the Board shall not lend, transfer or assign the license to any other person save with the prior permission of the Board. (2) A person who contravenes the provisions of sub 10 regulation (1) commits an offence and shall, on conviction, be liable to a fine not less than one million shillings or to an imprisonment of a term not less than twelve months or to both. PART V QUALITY ASSURANCE AND INSPECTION Appointment of quality assurance officers 24.-(1) The Board shall appoint qualified and experienced persons to be quality assurance officers. (2) The qualifications for quality assurance officers shall include- (a) a diploma or above in agriculture or relevant field; (b) working experience of not less than two years in an agricultural or relevant field preferably in the sisal industry; (c) any other relevant qualifications as the Board may deem fit. Functions of quality assurance officers 25. The quality assurance officers shall perform the following functions- (a) monitor the adherence of good crop husbandry practices by growers; (b) ensure proper and safe use of fertilizers, herbicides, fungicides and insecticides by growers; (c) monitor water utilization and environmental management in respect of sisal or sisal products; (d) carry out leaf potential estimates in sisal farms which might involve undertaking corona and field tests; (e) inspect and monitor leaf cutting, transportation, decortications, fibre drying, brushing, grading, baling and bales storage to ensure compliance with the prescribed standards; (f) inspect sisal fibre or sisal products in order to ensure compliance with prescribed standards; (g) inspect plant and machinery for processing or manufacturing of sisal or sisal products; (h) ensure that all sisal operations are carried out within acceptable established quantity and quality standards; (i) perform any other function as the Board may deem necessary. Powers of quality assurance officers 26.-(1) A quality assurance officer may, for the purposes of- (a) securing compliance with the provisions of these Regulations; (b) implementing directions given by the Board; or 11 (c) detecting and establishing any breach of any such provisions or directions: take samples of any sisal or sisal product found in any premises for conducting tests as may deem necessary. (2) A quality assurance officer may condemn or order re- grading of any consignment of sisal or sisal derivative which is found not to conform to approved grades or is not properly marked or is of poor quality standards. (3) A quality assurance officer may require the holder of any permit or license to produce books, records or returns in respect of production, stock returns, plantation returns, labour returns or sales returns for both local and export sales and research and development returns if any. (4) The quality assurance officer may at any time- (a) prior to the sale by the grower; (b) after processing; (c) prior to export, or (d) at the storage point; cause the review of any type of grade of sisal or its derivative- (5) Any person who obstructs a quality assurance officer in the exercise of the functions conferred upon him by these Regulations, or neglects or refuses to produce to the quality assurance officer any book or record which the quality assurance officer may request to be produced for inspection, commits an offence and shall, on conviction, be liable to a fine not less than five hundred thousand shillings or imprisonment for a term not less than six months. Grading, blending or marking of sisal and sisal products 27.-(1) A processor, manufacturer or trader shall ensure that sisal fibre or sisal products are properly graded or blended to comply with the approved standards and internationally recognized standards as prescribed in the Sixth Schedule to these Regulations; (2) A sisal manufacturer or processor shall use the mark registered with the Board for the grade or blend produced. (3) The following information shall be clearly displayed and visible on the mark specified in sub regulation (2):- (a) type of product, (b) date of manufacture and expiry if any; (c) grade or blend, (e) weight, (f) number, 12 (g) produce of Tanzania; and (h) any other information the manufacturer or processor may wish to display. Restriction on use of grades or blends 28.- (1) A person shall not trade in sisal or sisal products which is not of the recognized grades or blends and which does not comply with such blends or grades or defined types and characteristics or specifications. (2) A person shall not use any mark other than the mark registered in accordance with regulation 27(2). (3) Any person who fails to comply with the provisions of this regulation commits an offence. PART VI CONTRACT FARMING Contract farming 29. Subject to the provisions of Section 19A of the Act, a contract farming entered into by parties to the contract shall be in the prescribed standard form provided under the Seventh Schedule. Content of contract of farming 30.-(1) Subject to the requirements of Section 19A(2), a contract of farming shall contain- (a) clearly specified product under consideration; (b) clearly established prices, payment obligations and other financial obligations; (c) a dispute settlement clause; (d) a signature clause; (e) specified crop production estimates, corresponding input requirements and the price thereof; (f) witnesses to the contract; and (g) any other relevant information. (2) In the event a financier is desirous of entering into a contract with a grower having an outstanding debt from a different financier, the parties shall state in the contract, the method of repayment or offset of the debt and shall execute their agreement upon notifying the grower’s creditor Registration of contract of farming 31.–(1) The parties shall submit in quadruplet the contract farming agreement to the Board for registration within thirty days upon signing. (2) The Board shall verify and register the contract farming agreement and return copies to the respective parties. Failure to register a contract 32. A contract farming agreement which is not verified and registered by the Board shall not be enforceable. 13 farming agreement Dispute settlement mechanism 33. Where any dispute arises between the parties in respect of provisions of the contract farming agreement, it shall be settled as provided for in the dispute settlement clause of the respective agreement. Review of standard form contract 34. The contract farming model as provided in the Seventh Schedule may be reviewed and agreed by key stakeholders in the stakeholders meeting. PART VII SALES AND MARKETING Board to announce indicative price 35.-(1) The Board shall, after consultation with other stakeholders, announce indicative price for buying sisal every after three months which shall be used as a minimum price. (2) Subject to the provisions of sub regulation (1), negotiations for the establishment of the actual price of sisal shall be done between growers, or processors or manufactures on one hand and traders on the other. (3) The actual price arrived at under Sub regulation (2) shall not be below the indicative price. PART VIII STAKEHOLDERS FORUM Composition of annual stakeholders meeting 36. (1) There shall be an annual stakeholders meeting which shall be composed of key stakeholders of the Sisal industry. (2) The key stakeholders shall constitute members of an annual stakeholders meeting. (3) The composition of members of an annual stakeholders meeting shall be as prescribed in the Ninth Schedule. Role of stakeholders meeting 37. Subject to the provisions of section 8A of the Act, the roles of the stakeholders meeting shall be to- (a) deliberate and make resolutions on matters arising from the agenda and during the meeting; (b) form committees and working groups for the better carrying out the shared functions; (c) deliberate and determine indicative price for Sisal; (d) implement any other matter for sustainability and stability of the Sisal industry. Procedures for stakeholders meetings 38. Stakeholders shall adopt or review stakeholders meeting procedures as prescribed in the Ninth Schedule. 14 Implementation of stakeholders meeting resolution 39. The stakeholder’s secretariat shall be responsible to follow up and coordinate the implementation of all stakeholders meeting resolutions. PART IX SHARED FUNCTIONS BY LOCAL GOVERNMENT AUTHORITIES Matters for consideration by Local government authorities 40. Subject to the provision of section 20A of the Act, the Local government authorities shall, in the implementation of the shared functions agreed by stakeholders, take into consideration and ensure the following- (a) increased production of the sisal in their respective areas; (b) proper farming and husbandry of sisal; (c) maintenance of quality of sisal from production to market level; (d) proper maintenance and use of sisal feeder roads. Implementation of stakeholders meetings resolutions by Local Government Authorities 41. For the purpose of ensuring implementation of resolutions in the stakeholders meetings, Local government authorities may present their implementation report in the annual stakeholders meeting. PART XI MISCELLANEOUS PROVISIONS Strategic Plan 42.The Board in collaboration with other stakeholders shall develop a crop strategic plan in which it shall draw its strategic action plan Power to issue guidelines 43. The Board may issue guidelines for the effective implementation of the Act and these Regulations in relation to production, processing, marketing, exportation and importation of sisal. Board to develop staff performance standards 44.-(1) The Board shall develop staff performance evaluation scheme, which shall be based on physical achievement of the set out targets derived from strategic action plan. (2).For the purpose of evaluating staff performance, the Director General shall assess his employees using the prescribed form provided under the Tenth Schedule. Value chain analysis in service delivery 45. In ensuring compliance to standards of service to stakeholders, the Board in collaboration with other key stakeholders shall observe that- (a) the roles and responsibilities of each actor in the Sisal industry contributes to adding value to the development of the Sisal industry; (b) the potential for adding value through the means of cost 15 advantage or differentiation is enhanced; (c) the sisal industry attains sustainable competitive advantage. Information and data collection 46.-(1) A registered trader, processor, manufacturer, importer or exporter, shall where applicable, submit to the Board a monthly return of- (a) volume in tonnage of sisal purchased, processed, manufactured and price thereof; (b) amount and value of sisal exported or imported; and (c) any other information as the Board may deem necessary. (2) The monthly returns specified under sub regulation (1) shall be in the prescribed form provided under Eighth Schedule. (3) Every monthly return shall be submitted to the Board within fifteen days of the following month. (4) The Board shall compile and furnish a copy of the monthly report to the respective key stakeholders. (5)The Board shall maintain all statistical data and information relating to the sisal industry in the country. General offence and penalty 47. A person who contravenes any of these Regulations where no other punishment has been specified commits an offence and shall upon conviction be liable to a fine not exceeding five million shillings or to a term of not exceeding seven years or to both such fine and imprisonment. Corporate liability 48. Where any offence against these Regulations has been committed by any person with the consent or approval of a Director, manager, secretary or any other authorized officer with the capacity as a Director of that body corporate, shall be deemed to have committed the offence in the corporate name. Compliance with the Act 49. The provisions under these Regulations shall be construed in accordance with the provisions of the Act. 16 __________ FIRST SCHEDULE ___________ (Made under regulation 4(2)) ___________ SISAL CROP HUSBANDRY AGRONOMIC PRACTICES 1. Land clearing (hand and mechanical) Land should be brush cut followed by hand cleaning and burning in order to make it fit for mechanical cultivation. 2. Land preparation A thoroughly land preparation (reaping, ploughing, and harrowing) must be observed so as to reduce subsequent weed growth. Land preparation should normally be done before the onset of the rain season. 3. Nursery preparation Good planting material from nursery is an essential prerequisite for best sisal production. Bulbils nursery site must be well prepared and bulbils should be carefully collected and graded before planting. Sisal residues must be used as manure and mulch to improve soil fertility structure and aeration. 4. Planting: (a) Planting materials such as nursery seedlings are recommended at all times rather than suckers. Suckers can only be used under special circumstances and should be carefully selected and graded as stipulated by a competent sisal research institute. (b) Meristematic Tissue Culture sisal plantlets have proven to be best planting materials and they can be obtained from a competent sisal research institute (c) Before planting it is recommended to carryout soil analysis to determine soil fertility status of a particular field. (d) Fertilizer applications are essential for maintenance of soil fertility for subsequent high growth rate of plants. (e) Where applicable liming is essential for maintenance of soil PH for subsequent high growth rate of sisal plants. (f) Rotational planting programme should be followed closely because failure to plant in a particular year leads to reduction in leaf potential for production in the subsequent years. 5. Field maintenance: Immature sisal- For effective weed control weeding frequency should be at least four times per year. Herbicide application should be used as recommended by a competent sisal research institute. (a) Mature sisal- there should be at least three cycles of cleaning per year. (b) Fertilizer application, chemical/sisal residues application should be done as recommended by a competent sisal research institute. (c) Cover crops should be used e.g. - Tropical kudzu, Dolichos beans- (Ngwasha), Centrosema pubescens. Also other leguminous crops such as cowpeas, green gram, beans, soybeans and bambara groundnuts are recommended. 17 6. Cutting: (a) Timely cutting should be observed to reduce leaf loss which might be caused by Korogwe Leaf Spot. (b) Cutting programmes should be prepared in order to guide management on field operations and processing and it is essential to have an activity programme. 7. Processing: (a) Decortication: The decorticator should be finely tuned and have an efficiency of not less that 75% to ensure minimum loss of fibre, to attain this the decorticator must be frequently maintained by competent technicians. (b) Drying: Decorticated fibre should be dried within twelve hours after decortication so as to maintain its quality. (c) Brushing and Grading: The dried sisal fibre should be brushed after three hours after being lifted from drying lines. Brushing machines must be in good condition to produce good quality fibre. Brushing should be done to remove any hanging impurities and impart a shine to the fibre before it is baled for sale. Grading must be done properly and in accordance with internationally recognized standards of fibre classification. (d) Baling and storage Baling and press machines should be in good working condition to produce appropriate bales of different weights and sizes. The press machine needs to be checked and maintained regularly. Bales should be stored securely in a clean room or warehouse or godown. 8. The manner in which sisal shall be stored: Sisal fibre and products shall be stored in the following conditions: (a) The sisal fibre and products shall be stored in a cool dry place away from risks of fire hazards and other adverse weather conditions. (b) The warehouse or go-down where the sisal fibre and products shall be stored should be built in such a way that it does not allow rodents or vermin and floods to enter the premises. (c) The floor should not be smooth but rough so that workers are guaranteed safety i.e. they should not slip in the course of carrying out their duties. (d) The roof and rafters of the warehouse shall be high enough e.g. about 2-3 metres from the floor in order to maintain light and good aeration. (e) The ware-house or go-down shall have windows above the walls to allow light and air into the ware-house or go-down. 9. The manner in which such a storage place shall be maintained: The go-down/ ware house shall be maintained in such condition that- (a) Roof and floor should be in a condition that doesn’t allow leakage, i.e. no wall cracks and good roof. (b) Fibre and products must be placed in clean wooden/metal pallets and not direct on the floor. (c) Every grade must be placed on a distinguishable separate place. (d) Fibre or products should be placed 1 or 2 meters far from the wall and aside each other to allow safety inspection. (e) There will be no dirt, waste fibre or dust, which could pose risks of fire, attract insects and vermin (rodents). (f) The place should not store any inflammable materials such as fuel- kerosene diesel petrol and other hazardous materials. (g) The warehouse shall have five or more prevention equipment i.e. fire extinguishers, fire hydrants water hoses, sand buckets and water bower. 18 (h) The fire protection equipment shall be inspected yearly or any period as set by Government regulations. The equipment shall be inspected by the relevant authority. (i) There should be regular fumigation of a go-down or store to kill all insects and vermin such as termites, ants, snakes and mice in the premises. 10. Specification of the store: The store shall have enough space for storing the fibre or sisal products and to allow movement of the people in baling and staking, the minimum measurement should be 4m x 8m x 20m. The size may vary depending in the volume of business. 19 __________ SECOND SCHEDULE __________ (Made Under regulation 12) ____________ LAND UTILIZATION RETURNS TANZANIA SISAL BOARD P.O.BOX 277, TANGA FORM TSB -01 YEARLY SISAL LAND UTILIZATION REPORT (IN HECTARES) AS AT 31ST DECEMBER YEAR............… COMPANY /ESTATE TOTAL AREA MATURE SISAL IMMATURE TOTAL AREA OTHER CROPS OTHER LAND INFRASTRUCTURES FALLOW LAND SISAL UNDER SISAL NOTE: This form is to be completed in duplicate once a year only the ORIGINAL is to be submitted to TANZANIA SISAL BOARD. It must reach the addressee not later than 5th January of the following year. 20 TANZANIA SISAL BOARD P.O.BOX 277 TANGA Form TSB-02 LEAF POTENTIAL ASSESSMENT FOR YEAR........................................... NAME OF ESTATE................................. NAME OF COMPANY............................. YEAR HECTARES HECTARES CYCLES TOTAL HECT. METRES TOTAL METRES METRES TOTAL YIELD TONS PER PLANTED PLANTED TO BE CUT TO BE CUT TO BE CUT PER HECT. TO BE CUT PER TON IN TONS HECTARES Estate Manager's Name.........................................................Signature................................................................... Date........................ Note: This form is to be submitted to the Board before 5th January of each year. 21 TANZANIA SISAL BOARD P.O.BOX 277, TANGA Form TSB-03 COMPANY/ESTATE MONTHLY PLANTATION REPORT (IN HECTARES) FOR THE MONTH OF ...............YEAR....... EXISTING HECTARES TO 31ST DECEMBER PREVIOUS YEAR HECTARES PLANTED THIS MONTH NEW HECTARES PLANTED THIS YEAR TO DATE MATURE SISAL CUT OFF THIS MONTH MATURE SISAL CUT OFF THIS YEAR TOTAL EXISTING HECTARES END OF THIS MONTH NURSERY IMMATURE SISAL MATURE SISAL OTHER CROPS OTHER LAND INFRACTURE FOLLOW LAND TOTAL AREA NAME..............................................................DESIGNATION......................................................................SIGNATURE............................... VERY IMPORTANT This form is to be completed in duplicate the ORIGINAL is to be submitted to TANZANIA SISAL BOARD. It must reach the addressee not later than the 5th of the following month. 22 TANZANIA SISAL BOARD P.O.BOX 277, TANGA Form TSB-04 LABOUR RETURN FOR THE MEONTH OF ………………………………20………………… COMPANY/ ESTATE TOTAL NUMBER EXISTING AT THE BEGINNING OF THE MONTH NEW EMPLOYEMENT DEVELOPMENT TOTAL NUMBER EXISTING AT THE END OF THE MONTH PERMANENT CASUALS PERMANENT CASUALS PERMANE NT CASUAL S PERMANENT CAS UA LS NAME............................................................DESIGNATION...............................................................SIGNATURE........................ VERY IMPORTANT This form is to be completed in duplicate the ORIGINAL is to be submitted to TANZANIA SISAL BOARD. It must reach the address not later than 5th the following month. 23 ____________ THIRD SCHEDULE ____________ (Made under regulation 13(1)) _____________ APPLICATION FORMS FOR REGISTRATION TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org _______________________________________________________________ _____ Form TSB-05 SN............ To: Director General, Tanzania Sisal Board APPLICATION FORM FOR REGISTRATION (Made under regulation 13(1) of the Sisal Industry Regulations 2011) (1) PARTICULARS OF APPLICANT I/We......................................................................................................................................... ...of P.O. Box ..............................Tel:......................................Fax:................................................... E-Mail Address:...........................................................Website Address................................... Owner.....................................................................Sisal Fibre / Product Processing Plant with ........................................................................situated at............................................................ in......................................................District..................................................................Regio n. (2) GROWING / PROCESSING / MANUFACTURING PLANT DETAILS: (a) The name of the estate / factory is:............................................................................................. (b) Its designed production / processing capacity is:.................................................tons of .............................................................................................................................. .............................................................................................................................. ..(Mention Product) (c) Factory processing standard:...................................................................................................... .................................................................................................(Mention Product Grades) (d) Number of employees Skilled Workers:............................................... Semi Skilled Workers:..................................... Non-Skilled Workers:...................................... I/We intend to operate for.........................hours per day and produce so many units of ...................................................................................................................(Mention product) 24 (3) DECLARATION: I/We hereby declare that the farm / plant has been dully inspected and passed by the Board Quality Assurance Officers as per attached report No:...............................................date issued by the Board..................................................... I/We declare that I/We shall abide by the regulations and conditions governing the growing / processing of sisal as provided under Sisal Industry Act and its Regulations. ..................................... ...................................... .................................. Signature Designation Stamp 25 TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-06 SN.......... To: Director General, Tanzania Sisal Board APPLICATION FORM FOR REGISTRATION OF TRADERS (Made under Regulation 13(1)) (1) PARTICULARS OF APPLICANT I/We......................................................................................................................................... ...of P.O. Box ..............................Tel:......................................Fax:...................................................... E-Mail Address:...........................................................Website Address...................................... with office situated at....................................................................................................... in......................................................District..................................................................Regio n. (2) TRADING DETAILS: (a) The name of the company / sole proprietor is:.................................................................................................... (b) Its projected purchasing level is:..............................................tons of........................................ .............................................................................................................................. .............................................................................................................................. ..(Mention Product) (c) Its projected market is: Local / Export. ....................................................... .................................................................................................(Tick & Mention Products / Grades) (d) Number of employees Skilled Workers:............................................... Semi Skilled Workers:..................................... Non-Skilled Workers:...................................... (3) DECLARATION: I/We declare that I/We shall abide by the regulations and conditions governing the trading of sisal and sisal products. ......................................... ............................................ .................................. Signature Designation Stamp 26 This is to certify that M/S………………………………………………. was on……………………. registered with Tanzania Sisal Board for a period of two years, in accordance with the Sisal Industry Act No.2 of 1997 and subject to the terms and conditions of sisal growing / processing/ trading license and as per the Sisal Industry Regulations 2011 and issued with the following:- GRN:……………………. PRN:……………………. MRN:…………………… TRN:……………………. ………………….. Director General 27 ________ FOURTH SCHEDULE ________ (Under Regulation 19(1) __________________ APPLICATION FORMS FOR LICENSES TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-07 SN............. To: Director General, Tanzania Sisal Board APPLICATION FORM FOR SISAL PROCESSING / MANUFACTURING LICENSE (Made under Regulation 19(1)) (1) PARTICULARS OF APPLICANT I/We......................................................................................................................................... ...of P.O. Box ..............................Tel:......................................Fax:.................................................. E-Mail Address:...........................................................Website Address.................................. Owner.....................................................................Sisal Fibre / Product Processing Plant with ........................................................................situated at............................................................ in......................................................District.........................................................Region. (2) PROCESSING / MANUFACTURING PLANT DETAILS: (a) The name of the factory is:................................................................................................ (b) Its designed processing capacity is:.................................................tons of ...................... .....................................................................................................(Mention Product) (c) Factory processing standard:............................................................................................. ..........................................................................................(Mention Product Grades) (d) Number of employees Skilled Workers:................................................. Semi Skilled Workers:....................................... Non-Skilled Workers:......................................... I/We intend to operate for.........................days and produce so many units of ................................................................................................................... (Mention product) (3) DECLARATION: I/We hereby declare that the plant has been dully inspected and passed by the Board’s Quality Assurance Officers as per attached report 28 No:...............................................date issued by the Board..................................................... I/We declare that I/We shall abide by the regulations and conditions governing the processing of the said sisal product. ..................................... ...................................... .................................. Signature Designation Stamp Duty Note: See conditions overleaf. C0NDITIONS FOR OBTAINING A LICENSE FOR PROCESSING SISAL (a) An applicant must own a processing mill / plant. Proof of ownership must be produced. (b) The mill / plant must have been inspected and approved by the Board’s Quality Assurance Officers or an agent thereof. (c) It is the responsibility of the processor / miller of the plant to cause Quality Inspection Officers from the Board or its agents to inspect and certify the milling / processing plant for issuance of the milling / processing license. (d) The validity of the applied license shall end 31st December each year. NB: No miller / processor shall operate a milling / processing plant without a valid milling /processing license. 29 TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-08 SN......... To: Director General, Tanzania Sisal Board APPLICATION FORM FOR SISAL TRADING LICENSE (Made under regulation 19(1)) 1) Particulars of Applicants: I/We......................................................................................................................................... ...of P.O. Box ............................and holders of Business License No:..................TRN.................... (attached) issued at...............................................................wish to apply for sisal fibre / products Trader’s License. 2) Areas of Operation I/We ...........................................................................intend to operate in.....................District in .....................................................Region 3) Financial Arrangement: I/We confirm that, I/We will have no problems with financing and same can be confirmed by my/our bankers:...................................................................................................................... of P.O. Box...........................Phone:..........................................Fax:...................................... 4) Declaration: I/We declare that I/We shall abide by the regulations governing the procurement of sisal fibre / products from farmers / processors as issued and as will be directed by the Board from time to time .......................................... ................................... .................................. Signature Designation Official Stamp Note: See conditions overleaf 30 ________ FIFTH SCHEDULE __________ (Under regulation 18(2)) ___________ TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-09 SN........ SISAL PROCESSING LICENSE (Made under Regulation 18(2)) License No................................ is hereby granted to M/s.......................................................................................................... Reg. No...............................of P.O. Box................................................................................ To process Sisal at the factory’s premises located at..............District in..................Region Issued on:...................................................................... Expires on:.................................................................... Issued at:...................................................................this day of:..................................... Authorizing Officer...................................................... Signature...................................................................... Designation.................................................................. Date:............................................................................ Official Stamp/Seal:.................................................... Note: See conditions overleaf. 31 CONDITIONS FOR SISAL PROCESSING LICENSE (a) The processor shall obtain and display a valid processor’s license in an easily accessible place in a conspicuous manner. (b) The processor shall maintain and work a processing facility in a proper manner as designated, in such a way as to obtain best quality sisal fibre. (c) The processor shall ensure that all processed sisal is correctly graded as per the sisal grades definitions of the Board. (d) The processor shall provide monthly reports to the Board on all information pertaining to quantity, quality and dispatches of processed Sisal. (e) The Board may exercise its powers in accordance with the Tanzania Sisal Industry Act No. 2 of 1997 and Regulations to cancel or suspend a processing license if a licensee fails to comply with terms and conditions of the license. (f) This license is not transferable. (g) The validity of the license shall end 31st December each year. 32 TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-10 SN......... SISAL MANUFACTURING LICENSE (Made under ection Regulation 18(2)) License No........................................... is hereby granted to M/s....................................................................................................... Reg. No...............................of P.O. Box................................................................................. To spin /manufacture sisal:...................................................................................................... ..............................................................................................(Mention Products) At the factory’s premises located at.............................District...................................Region Issued on:...................................................................... Expires on:.................................................................... Issued at:........................................................................this day of:..................................... Authorizing Officer...................................................... Signature...................................................................... Designation.................................................................. Date:............................................................................ Official Stamp/Seal:........................................................... Note: See conditions overleaf 33 CONDITIONS FOR SISAL MANUFACTURING LICENSE (a) The spinner / manufacturer shall obtain and display a valid manufacturer’s license in a conspicuous manner. (b) The spinner / manufacturer shall maintain and work the mill / plant in a proper manner as designated, in such a way as to obtain best quality sisal products. (c) The spinner / manufacturer shall ensure that all sisal delivered to the mill / plant is correctly graded as per the sisal grades definitions by the Board. (d) The spinner / manufacturer shall provide monthly reports to the Board on sisal products received showing grades, quantity and price in the forms prescribed by the Board. (e) The spinner / manufacturer shall ensure that sisal product packaging materials are dully labeled. (f) The Board may exercise its powers in accordance with the Tanzania Sisal Industry Act No. 2 of 1997 and Regulations to cancel or suspend a growing or buying license if a licensee fails to comply with terms and conditions of the license. (g) This license is not transferable. (h) The validity of the license shall end 31st December each year. 34 TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-11 SN.......... SISAL TRADING LICENSE (Made under ectionRegulation 18(2)) License No................................ is hereby granted to M/s................................................................................................................................ of P.O. Box................................................................................................................................ to trade in sisal in the United Republic of Tanzania Issued on:...................................................................... Expires on:.................................................................... Issued at:................................................................this day of:.............................. Authorizing Officer...................................................... Signature........................................................................ Designation........................................................................ Date:................................................................................... Official Stamp/Seal:........................................................... Note: See conditions overleaf. CONDITIONS FOR A SISAL TRADING LICENSE (a) Every Trader shall display this license in an easily accessible place in a conspicuous manner. (b) Every trader must be registered with the Board. (c) The Board may exercise its powers under the Tanzania Sisal Industry Act No. 2 of 1997 and Regulations to cancel or suspend a trading license if a licensee fails to comply with terms and conditions of the license. (d) This license is not transferable. (e) The validity of the license shall end 31st December each year. 35 TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-12 SN.......... SISAL EXPORT LICENSE (Made under ection Regulation 18(2)) License No................................... is hereby granted to: M/s.......................................................................................................................................... ....of P.O. Box:................................................................................................... To export sisal fibre/products........................................................................................................................... ................................................................................................................................................. ..............................................................................................(Mention Products) Issued on:...................................................................... Expires on:.................................................................... Issued at:................................................................this day of:............................................. Remarks:................................................................................................................................ ................................................................................................................................................. ...... Authorizing Officer..................................................... Signature...................................................................... Designation................................................................... Date:.............................................................................. Official Stamp/Seal: Note: See conditions overleaf. 36 CONDITIONS FOR A SISAL EXPORT LICENSE (a) All sisal fibre/products must bear the correct label and the address of the exporter. The Board may exercise its powers in accordance with the Tanzania Sisal Industry Act No. 2 of 1997 and Regulations to cancel or suspend the exporter license if a licensee fails to comply with terms and conditions of the license. (b) Compliance with these Regulations does not exempt or preclude anyone from adhering to other procedures promulgated by the Government as far as export and import procedures are concerned. (c) Exporters shall keep records of all transactions and must submit returns to the Board. (d) This license is not transferable. (e) The validity of the license shall end 31st December each year. 37 TANZANIA SISAL BOARD P.O. Box 277 Tanga, Tanzania. TEL/FAX: +255 27 2645060 Tasma Road, Katani House, Ground Floor E-Mail: [email protected] Web Site: http://www.tsbtz.org Form TSB-13 SN.......... SISAL IMPORT LICENSE (Made under ection Regulation 18(2)) License No................................... is hereby granted to: M/s.......................................................................................................................................... ....of P.O. Box:................................................................................................... To import sisal fibre/products........................................................................................................................... ................................................................................................................................................. ..............................................................................................(Mention Products) Issued on:...................................................................... Expires on:.................................................................... Issued at:................................................................this day of:............................................. Remarks:................................................................................................................................ ................................................................................................................................................. ...... Authorizing Officer..................................................... Signature...................................................................... Designation................................................................... Date:.............................................................................. Official Stamp/Seal: Note: See conditions overleaf. 38 CONDITIONS FOR A SISAL IMPORT LICENSE (a) All sisal fibre/products must bear the correct label and the address of the importer. The Board may exercise its powers in accordance with the Tanzania Sisal Industry Act No. 2 of 1997 and Regulations to cancel or suspend the importer license if a licensee fails to comply with terms and conditions of the license. (b) Compliance with these Regulations does not exempt or preclude anyone from adhering to other procedures promulgated by the Government as far as export and import procedures are concerned. (c) Importers shall keep records of all transactions and must submit returns to the Board. (d) This license is not transferable. (e) The validity of the license shall end 31st December each year. 39 SIXTH SCHEDULE ______________ (Made under regulation 27(1)) __________________ APPROVED GRADES BY TANZANIA SISAL BOARD (1) Grade 1-length, from 90cm upwards, free of defective decortications properly brushed, free of tow cutting and dust free whose colour may vary from creamy white to cream. (2) Grade A-length, between 75cm and 90cm upwards, free of defective decortications; properly brushed; free of tow; cuttings and dust; free of tousled and bunchy ends whose colour may be slightly yellowish. (3) Grade 2-length, between 75cm and 90cm, free of defective decortications, properly brushed, free of tows, cuttings and dust, free of tousled and bunchy ends whose colour may vary from creamy white to cream. (4) Grade 3L- length minimum 90cm, free of defective decortications, properly brushed free of tows, cuttings and dust, free of tousled and bunchy ends whose colour may be slightly yellowish. (5) Grade 3S- length between 60cm and 90cm, free of defective decortications, properly brushed, free of tows, cuttings and dust, free tousled and bunch ends whose colour may vary from creamy white to slightly yellowish. (6) Grade UG (or R) length from 60cm to 90cm, free of defective decortications, properly bushed, free of tow, cuttings and dust, free of tousled and bunchy ends. Colour may vary from white to yellowish and light green. (7) Grade SSUG- Length should not be less than 60cm. This is the fibre which does not conform to standard UG grade. Colour may vary from yellowish to more darkish and blemished. (8) Grade UF-length from 60cm upwards, slightly barky runners and harshness are permissible. Brushed and unbrushed fibre is allowed. Free of dust and under corticated barks. Colour may be blemished, greenish, yellowish, brownish due to delayed decortications and blackish but not rotten. (9) Grade S.C.W.F – Short Clean white fibre: Length between 45cm and 60cm. Free of defective decortications, properly brushed, free of tow, and dust free tousled and bunchy ends colour may vary from creamy white to cream. (10) TOW 1- Pieces of fibre that comes out behind the brushing machines. Entirely free of fine fibre, dust, sweeping knots and undercoticated barks. Colour may vary from creamy white to cream. (11) TOW 2 –Pieces of fibre that comes out behind the brushing machine entirely free of line fibre, dust, sweeping knots and under corticated barks. Colour may be yellowish, greenish, and brownish. (12) Flume Tow – Fibre cuttings extracted from the flume channel and dried. (13) UHDS – length 60cm upwards and its fibre, which comes from unwashed hand, decorticated sisal. It can be brushed or not brushed and colour may be blemished, greenish, brownish and blackish but not rotten. 40 ________ SEVENTH SCHEDULE STANDARD FORM AGREEMENT FOR CONTRACT FARMING _____ (Made under Regulation 29) _____ This agreement is made on …………………day of …………………..20...………… BETWEEN (Registered farmer herein referred to as the Grower) Full Name………………………………………………………. Address:………………………….Tel…………………………..Email…………………. Factory location:…………………………………..District…………………………….. Registration No……………………………………… Farm details: Location …………………Acreage (under tea)……………………… AND (Sisal buyer/Sisal processor/Sisal investor/Banker herein referred to as Financier) Full Name………………………………………………………. Address:………………………….Tel…………………………..Email…………………. *Factory location:…………………………………..District…………………………….. *Registration No……………………………………… Preamble; WHEREAS the Grower is desirous to grow and sell sisal in his field located at …………………………………………………………………………………………………… ………………………………………………………………………………………… WHEREAS the Financier is desirous to ……………………………………. …………………………………………………………………………………………………… ………………………………………………………………………………………… AND WHEREAS the grower is desirous to access ....................... (Hereinafter referred to as “facility”) for sisal growing activities and the financier is willing to provide ……………………….. to the grower on terms and conditions set forth in this agreement NOW THEREFORE the parties agree as follows: 41 A: Joint undertaking 1. The contract shall operate from…………….………….to ………………………and may be extended on mutually agreed terms and conditions 2. Any alteration, extension or renewal of the contract shall form an addendum to this Agreement and shall be submitted to the Board for approval and registration. 3. This contract is not transferable and cannot be assigned save as agreed by the parties. 4. A sale agreement drawn between the parties, if any, shall be countersigned in four copies and delivered to the Board as an appendix to this contract. 5. The parties to the contract shall adhere to their obligations in the contract and failure of which, a party in default shall be required to remedy the other. 6. The parties shall agree and observe provisions on internal governance B; The Grower undertakes I. SPECIFIC………………………………………………………………………………. …………………………………………………………………………………………….. II.GENERAL 1. Not to enter into any contract with any other financier, content of which is similar to the subject matter of this contract 2 To disclose and give status on any previous or existing contractual obligations. C: The Financier undertakes I. SPECIFIC……………………………………………………………………………… ……………………………………………………………………………………………. II. GENERAL 1 Not to enter into any contract with any other financier, content of which is similar to the subject matter of this contract 2. To disclose and give status on any previous or existing contractual obligations where applicable . E: Law Applicable 1. This agreement shall be governed by the Laws of the United Republic of Tanzania. F: Dispute resolution 1. Any dispute arising between parties to this Contract shall in the first instance be mediated by the Board, failure of which may entitle either party to seek other remedies in accordance with the laws of Tanzania G: Annextures 1. The following documents shall form part of this contract ……………………………………………………………………………………. 42 …………………………………………………………………………………… IN WITNESS WHEREOF the parties have executed these presents on the date first above written and in the manner appearing herein below; Grower Witness Full Name: _____________________________Full Name: _______________________ Signature: _______________________________Signature________________________ Date: ___________________________________Date: ___________________________ Financier Witness Full Name: _____________________________Full Name: _______________________ Signature: _______________________________Signature________________________ Date: ___________________________________Date: ___________________________ Copies to be provided to: The Board, District Council, Association, Buyer and Grower Attachment: List of members of the growers represented under this contract. FOR OFFICIAL USE ONLY Approved/Not approved by the Board Reasons...………………………………… Registration No………………………….. Signature ………………………………… Designation…….………………………… Seal………………………………..……… Date…………………………..………….. District ............................. Country.............................................................. Village ............................................ Area..................................................... No. of Growers …….. ................. Grower’s Registration No. .................... Date of Signature ........................... Date of Recording ............................... No. of farmers under the association/cooperatives grouped under the member village appendix with each member farmer signature……………………….. 43 EIGHTH SCHEDULE _________________ (Made Under Regulation 46 (2)) 2. EXPORT SALES (IN TONS) FOR THE MONTH OF ..................20.....… DATE CONTRACT NO. GRADE TONS PRICE(FOB/CIF) DESTINATION SHIPMENT PERIOD 2. LOCAL SALES (IN TONS) FOR THE MONTH OF ..................20.....… DATE CONTRACT NO. GRADE TONS PRICE (FOB/CIF) DESTINATION SHIPMENT PERIOD TANZANIA SISAL BOARD Form TSB-14 P.O.BOX 277, TANGA COMPANY/ESTATE....................... DATE..............20......... 1. PRODUCTION AND STOCK (IN TONS) RETURN FOR THE MONTH OF.................20.….. DESCRIPTIONS GRADES No.1 N o. A No. 2 3 L 3 S UG TOTAL TO W1 TO W2 TOTAL UF FLU ME GRAN D LINE FIBRE TOWS TO W TOTA L Stock at estate on 1st.........20..... Produced during the month Total Dispatched during the month Stock at estate end of .......20... 44 3.PRODUCTION ESTIMATES FOR JANUARY TO DECEMBER 20........(IN TONS) MONTHS GRADES No.1 No.A No.2 3L 3S UG TOTAL TOW1 TOW2 TOTAL UF FLUME LINE FIBRE TOWS TOW 1 2 3 4 5 6 8 9 10 11 12 4. BALING RATE: LINE FIBRE ...........BALES PER TON TOW..................................... BALES PER TON FLUME TOW........................ BALES PER TON UF...................................... BALES PER TON 5.CERTIFICATION: We certify that the production as declared above has been brushed, graded and baled is good and marketable. Yours faithfully Signature...................................................................Name.................................................................................................... Designation...........................................................................................Official Rubber Stamp......... 6.VERY IMPORTANT This form is to be completed in duplicate. The ORIGINAL is to be submitted to the TANZANIA SISAL BOARD. It must reach the addressee not later than the 5th of the following month. 45 TANZANIA SISAL BOARD P.O.BOX 277, TANGA Form TSB-15 MONTHLY SISAL FIBRE EXPORTS AND LOCAL SALES REPORTS FOR THE MONTH OF..........20........ COMPANY/ESTATE............................ 1. SISAL FIBRE EXPORT SALES S/NO. GRADE TONS USD PER TON TOTAL VALUE BUYER DESTINATION IN USD 2. SISAL FIBRE LOCAL SALES S/NO. GRADE TONS TSHS PER TONTOTAL VALUE BUYER DESTINATION IN TSHS 46 VERY IMPORTANT This form is to be completed in duplicate the ORIGINAL is to be submitted to TANZANIA SISAL BOARD. It must reach the addressee not later than the 5th of the following month. _____________ NINTH SCHEDULE ______________ (Made under regulation 38) _______________ PROCEDURES FOR SISAL STAKEHOLDERS FORUM 1. INAUGURATION 1.1 The inaugural meeting shall deliberate and approve draft procedures for conduct of stakeholders’ forum and subsequent operation and below is the framework for general guidance purposes only. 2. ORGANOGRAM 2.1 The forum is the apex organization in the Sisal Industry whereby all players in the industry have an opportunity to be heard. It creates an ownership of shared vision and identifies ways and means to finance and execute shared functions .The stakeholders shall provide the Chairman and the Board will provide the Secretariat. 3. COMPOSITION 3.1 The composition of the stakeholders of the sisal industry shall comprise of the Lead Ministries, growers including smallholder farmers, processors, manufacturers, traders, researchers, relevant academic institutions, local government authorities, associations, cooperative societies, trusts and service providers; 3.2 The representation of the stakeholders in the Annual Stakeholders Meeting shall be as follows:- 3.2.1 Two (2) representatives from the Ministry responsible for Agriculture 3.2.2 One (1) representative from each Ministry responsible for:- i. Industries and Trade ii. Energy and Minerals iii. Finance and Economic Affairs iv. Regional and Local Government Authorities 3.2.3 Fifteen ()epresentatives of the Sisal Association of Tanzania 3.2.4 Five ()epresentatives of Sisal Smallholder Farmers 3.2.5 Two ()representatives of a relevant sisal research institution 3.2.6 Two () representatives of Academic Institutions 3.2.7 Any other representatives as agreed upon by the stakeholders 4. NOTICE AND AGENDA 4.1 The Board shall schedule a regular Stakeholders Forum at least once a year. 4.2 The Board shall provide the secretariat services for the sisal stakeholders forum. 47 4.3 The notice shall be vide a public media, email, fax and telephone. 4.4 Meeting date, time and agenda to be communicated and confirmed at least fourteen days prior to the meeting. 4.5 Members shall be invited to transmit agenda items to the secretariat. 4.6 Draft minutes of the previous meeting to be circulated to members before the meeting and the final confirmation of the minutes to take place at the meeting. 5. ELECTION OF CHAIRPERSON 5.1 The Ministry responsible for Agriculture shall designate a Chairperson for the inaugural meeting. 5.2 The Stakeholders Forum shall elect the Chairperson. 5.3 The elected chairperson shall serve for a period of three years at a maximum of two terms then shall take a break of two years before he becomes eligible for re-election. 5.4 The chairperson shall preside over the meetings. In his absence the present members who form a quorum of a minimum of fifty percent shall wait for the period of thirty minutes then shall elect an interim Chairperson. 5.6 Whenever a need arises the Forum can delegate specific issues to working groups comprising of specialists members. All findings and outcomes of the working groups shall be made available to the Forum for decision-making and approval. 5.7 The Chairperson shall be the official spokesperson of the Forum 6. ATTENDANCE/PROXY 6.1 Membership of meetings is institutional therefore proxies will be accepted at the meetings. 6.2 An absent member wishing to vote may appoint a proxy who will vote on his behalf. No proxy can represent more than two members of the Forum. 7 GENERAL 7.1 All members in attendance at the Forum shall be accorded equal opportunity to contribute to the meeting. 7.2 The Board shall incorporate the business of the Stakeholders Forum in their Annual Reports. 48 Form TSB-16 ___________ TENTH SCHEDULE ___________ (Made under regulation 44 (2)) ___________ TANZANIA SISAL BOARD P.O. BOX 277, TANGA. STAFF APPRAISAL FORM (Prepared under TSB Staff Services Regulations) ATTENTION: (1) This form MUST be completed annually by all staff even those still under probation with an exception of those serving on temporary and casual terms. (2) Part of the form shall be completed by the staff himself or herself and the other to be completed by his or her supervisor. SECTION 1: EMPLOYEE’S PARTICULARS (To be completed by employee) Name: (in full) ………………………………………….. Job Title: ……………………. Department/Directorate: ………………………………. Section: ………………………. Present station: ………………… Basic salary ……………………. Salary Scale ……... Incremental date: …………………………………………… Terms of service ……………………………………………. I have been under my reporting supervisor for ………………. months engaged on the work of ………………………………………………………………... Dar es Salaam, JUMANNE A. MAGHEMBE ………………, 2011 Minister for Agriculture, Food Security and Co-operatives

# Extracted Content TABLE OF CONTENTS 1. ACRONYM ………………………………………. ………………………. .. i 2. EXECUTIVE SUMMARY ………………………………………………….. 1 3. INTRODUCTION ……………………………….. …………………………2 – 3 4. PROJECT IMPLEMENTATION PLAN/ PHYSICAL AND FINANCIAL FRAMEWORK/ KEY ACTIVITIES AND TARGETS, OUTPUTS AND ITS INDICATORS (ACTIVITIES PLANNED, ACTIVITY ACHIEVEMENT, DEVIATION AND REASONS FOR DEVIATIONS ………………………………………………………………………………………. 4 – 9 5. DASIP ANNUAL FINANCIAL REPORT ……………………………………………………10 - 13 6. PROJECT OUTPUT/RESULTS FOR COMPLETED PROJECTS ..............14- 16 SUCCESS STORY ……………………………………………………………………….17 7. IMPLEMENTATION CHALLENGES FACED BY A FARMER ……………………………..18 8. STRATEGIES AGAINST EXISTING CHALLENGES …………………………………………. 18 9. GENERAL IMPLEMENTATION CHALLENGES OF DASIP ACTIVITES ……………….. 19 10. RECCOMENDATIONS …………………………………………………………………………….. 19 1 ARONYMS 1. WFT …………………………………… Ward Facilitation Ream 2. FFS …… ……………………………… Farmer Field Schools 3. PFG …………………………………… Participatory Farmer Groups 4. WDC…………………………………… Ward Development Committee 5. VDC …………………………………... Village Development Committee 6. DASIP ………………………………… District Agriculture Investment Project 7. DADP …………………………………. District Agricultural Development Plan 8. DTC ……………………………………. District Training Coordinator 9. DMEO ……………………………….. District monitoring and Evaluation Officer 10. DPO ……………………………………. District Project Officer 2 1.0 EXECUTIVE SUMMARY Ukerewe District Agricultural Investment Project (DASIP) is being implemented in 20 wards and involves 30 villages. For the financial year 2009/2010, the council has implemented 6 activities of 2007/08 that was delayed against plan, 12 investment activities that were planned in the financial year 2008/09 and 4 investment activities for the year 2009/10. Other activities were, Office operation and maintenance, Formation of 2009/10 Participatory Farmer Groups (FFs), Season long training of 180 Participatory Farmer Group, (PFGs) training of Farmer Facilitators and monitoring/supervision of DASIP activities in the implementing villages. The total budget for the financial 2009/2010 was Tshs 503,399,000/=, the balance resulted from delayed implementation of activities from financial year 2008/09 was Tshs 68,189,192. The total funds received for implementation of planed activities in 2009/10 was Tshs 543,334,000/=. However, Tshs 496,610,700/= (91.4%) of the funds disbursed has been spent as on 30 June 2010. The remaining balance is Tshs 114,912,492. During the implementation of different projects, various challenges were encountered among which are, Some of the completed projects are semi or completely not operational due lack of polical push resulting from clossness to erection time, Community contribution of 20% requires etra effort resulting into untimely accomplishing planned activities e.g purchase of power tillers, Limited resource funds for monitoring and follow up reduces the suppervision frequence so leading to failure in rectifying mistakes at early stages, Limited number of extension staff leads to untimely formation of PFGs. 3 Introduction DASIP project is beeing implemented in 30 villages in 20 wards of Ukerewe district. The planned activities for the investment component in the year 2009/2010 were 13 being, construction of farmers market sheds, shallowells construction, storage structure and rehabilitation of rural feeder roads costing Tshs 375,554,375/= from DASIP and community contribution. Other projects were implemented using carry over funds i.e Seven (7) farmer market sheds were constructed,1 culvert and 2 feeder loads were rehabilitated. Of all the projects, only 5 have been completed to 100% remaining with 18 projects at different stages of completion. On the other hand, the LGA planned to purchase agriculture value addition equipments among which are, 8 power tillers, 10 irrigation water pumps, 5 fruit processing machines, 3 cassava graters and 8 milling machines. A total of Tshs 55,000,000/= has been disbursed to the LGA by DASIP for purchase of the mentioned equipment. Efforts has been made by the district to sensitize the groups on their role towards contributing 20% to enable them to aquire the said equipment(s). This was done through routine council meeting, WDCs, VDCs, Group members meeting and the use of farmer facilitators. The process is underway. Upon farmer and extensionist capacity building, the council had a plan to train 180 PFGs through FFS. Only 168 PFGs were formed and trainned crops and livstock production techniques as per June 2010. The reasons for deviation include, geographical complication, limited number of extension workers in the district due to retirement and death to mention a few. The types of enterprices were: Maize, sorghum, sunflower, orrangefleshed sweet potatoes, paddy, tomatoes,beans, Onion, and Sweet potatoes as crops enterprices while local chicken and pig were established on the side of livedstock enterprises. Also 18 WTF and 26 FFs were trainned on busnessplan preparation. All PFGs measured in crops, harvested their plots. Approaches used to collect data/information Data collection imvolved the use of monthly reports from extension officers, Reports from farmers facilitators, reports from contractors, reports from village commitee and direct dialoque with farmers during monitoring and suppervision of DASIP activities. Council objective: Using the DASIP project, the distirict council is aiming at ™ Increasing the income of the people living in the villages where DASIP operates using the completed projects. ™ Improve transport and transportation of farm products and produce from farms to the area of disposal eg. Consumption and market, hence reduced cost of production ™ To improve animal health through water availability for animal drinking using the shallow wells. ™ Improve food security through irrigation water provided by shallow wells in place 4 5 Achivements More than 60% of farmers trainned through FFS has managed to increase productivity in their own fields, a call for more trainning on different enterprices. 6. THE PROJECT IMPLEMENTATION PLAN Activities planned to be implemented in the year 2009/10 are as follows: 6.1. Farmer Capacity Building. (a) Season long training of 180 PFGs (b) Facilitation of 180 PFGs undertake economic mini-enterprises (c) Close follow up on WFTs and FFs training programmes and their schedule for FFS Trainings 6.2. Community Planning and Investment in Agriculture. (a) Consolidation of VADPs to get 2010/11 DADP (b) Continue with implementation of the ongoing projects from previous fiscal years (c) Monitoring and supervision of completed, on going and new project activities 1 PROJECT IMPLEMENTATION PLAN/ PHYSICAL AND FINANCIAL FRAMEWORK/ KEY ACTIVITIES AND TARGETS, OUTPUTS AND ITS INDICATORS (ACTIVITIES PLANNED, ACTIVITY ACHIEVEMENT, DEVIATION AND REASONS FOR DEVIATIONS BUDGET ‘000’ Na. PROJECT NAME PLANNED ANNUAAL TARGET PLANN ED YEAR FOR COMPL ETION IMPLEM ENTATI ON STATUS BY JUNE 2010 % ANNUA L BUDGE T RECEIVED EXPEND ITURE BALA NCE DEVIATION AND REASONS FOR DEVIATION 1 Construction of market shed at Chankamba village Expansion of market shed to comply with the new drawings 2007/200 8 At finishing stage 90 28,000 28,000 22,330 3,660 Procurement procedures and low capital of the awarded contractor 2 Construction of market shed at Kazilankanda village Expansion of market shed to comply with the new drawings 2007/200 8 At finishing stage 95 28,000 28,000 19,248 8,751 Unfaithfulness of the contractor 3 Construction of market shed at Hamkoko village Expansion of market shed to comply with the new drawings 2007/200 8 Complete d 100 28,000 28,000 26,647 1,353 Completed as it wwas planned 4 Construction of market shed at Busumba village Completion of market shed as per new 2008/09 At slab stage 45 28,000 28,000 10,757 17,242 Incapable contractors 2 drawings 5 Construction of market shed at Bukiko village Completion of market shed as per new drawings 2008/09 At finishing stage 90 28,000 28,000 17,298 10,701 Poor responce of the community to contribute 20% 6 Construction of market shed at Kagunguli village Completion of market shed as per new drawings 2008/09 Roofing and plasterring has been completed 80 28,000 28,000 22,100 5,900 Poor responce of the community to contribute 20% 7 Rehabilitation of rural feederload at Busiri village To facilitate transport and transportation of farm produce 2008/09 Complete d 100 12,000 12,000 11,900 1,748 No deviation 8 Construction of 7 shallow wells at Kakerege village To facilitate agriculture irrigation practices and water for livestock 2008/09 Complete d 100 22,400 22,400 22,109 200 No deviation 9 Construction of culvert at kakerege village To facilitate transport and transportation of farm produce 2008/09 Complete d 100 5,600 5,600 5,600 0 No deviation 10 Construction of 3 shallow wells at To facilitate agriculture 2008/09 Complete 100 12,000 12,000 11,400 600 No deviation 3 Bukungu village irrigation practices and water for livestock d 11 Rehabilitation of rural feederload for Mwitibi – Kuchara at Bukungu village To complete the rehabilitation of the feederload 2008/09 Complete d 100 16,000 16,000 14,252 1,748 No deviation 12 Construction of market shed at Kamasi village Completion of market shed as per new drawings 2008/09 Roofing and office constructi on is complete 80 28,000 28,000 18,390 9,866 Poor responce of the community to contribute 20% 13 Rehabilitation of rural feederload at Sambi village To facilitate transport and transportation of farm produce 2008/09 Two culvert remained 95 26,080 26,080 23,213 2,866 Breakage of culvert by water runnoff 14 Construction of market shed at Igongo village Completion of market shed as per new drawings 2008/09 Complete d 100 28,000 28,000 26,649 1,350 No deviation 15 Construction of market shed at Chamuhunda village Completion of market shed as per new drawings 2008/09 At roofing stage 70 28,000 28,000 21,257 6,742 Delay in openning of Bank account by the village project commitee 4 16 Construction of market shed at Mukunu village Completion of market shed as per new drawings 2009/10 Roofing has been completed and plastering is being carried out 80 28,000 28,000 10,243 17,243 Delay in openning of Bank account by the village project commitee 17 Construction of market shed at Nyang’ombe village Completion of market shed as per new drawings 2009/10 At slab stage 30 28,000 28,000 10,415 17,584 Incapable contractors 18 Construction of market shed at Bukiko village Completion of market shed as per new drawings 2009/10 At slab stage 30 28,000 28,000 10,026 17,973 Poor responce of the community to contribute 20% 19 Construction of market shed at Busunda village Completion of market shed as per new drawings 2009/10 Roofing has been completed and plastering is being carried out 85 24,000 24,000 9,129 14,870 Poor responce of the community to contribute 20% 20 Construction of market shed at Namilembe village Completion of market shed as per new drawings 2009/10 At slab stage 30 28,000 28,000 10,757 17243 Poor responce of the community to contribute 20% 21 Construction of market shed at Kaseni village Completion of market shed as per 2009/10 At slab stage 30 28,000 28,000 3,249 24,751 Poor responce of the community to contribute 20% 5 new drawings 22 Rehabilitation of rural feederload at Muhande village To facilitate transport and transportation of farm produce 2009/10 Not yet 0 18,440 18,440 0 18,440 Funds allocated to accomplish work is not in favour of the contractors 23 Construction of storage facility at Murutanga village To facilitate storage of farm produce 2009/10 Not yet 0 28,000 28,000 0 28,000 The idea of changing the project brought about by the newly erected leaders 24 Construction of market shed at Nkilizya village Completion of market shed as per new drawings 2007/08 Complete d 100 28,000 28,000 28,000 0 No deviation 25 Construction of slaughter slab at Sambi village Completion of one slaughter slab 2007/08 Complete d 100 1,900 1,900 1,900 0 No deviation 26 Construction of slaughter slab at Bwasa village Completion of one slaughter slab 2007/08 Complete d 100 1,900 1,900 1,900 0 No deviation 6 DASIP- ANNUAL FINANCIAL REPORT 2009/10 DISBURSEMENT S/N ACTIVITY/ PLAN BUDGET 2009/10 BALANCE FROM 2008/09 DISBURSEMENT 2009/10 EXPENDITURE BALANCE AS AT 30/06/2010 1 Office operation and maintenance cost 1,500,000.00 962,267.00 1,500,000.00 1,509,200.00 953,067.00 2 Staff field allowances 8,750,000.00 103,500.00 8,750,000.00 8,399,600.00 453,900.00 3 DTCs field allowances 1,300,000.00 1,300,000.00 650,000.00 650,000.00 4 DTCs office operating expenses 1,200,000.00 960,300.00 1,200,000.00 2,059,000.00 101,300.00 5 Motorcycle allowances for DMEO and DPO 1,800,000.00 110,000.00 1,920,000.00 960,000.00 1,070,000.00 6 Motorcycle allowances for DTCs 1,800,000.00 10,000.00 1,920,000.00 960,000.00 970,000.00 7 Season long training of 40 participatory farmer groups 2007/08 - 4,114,500.00 - 4,089,000.00 25,500.00 8 Season long training of 180 Participatory Farmer Group 51,758,625.00 - 38,108,000.00 13,650,625.00 9 Formation of 2009/10 Participatory Farmer Groups (PFGs) 2,300,000.00 2,300,000.00 2,298,300.0 1,700.00 7 10 Participation of DASIP in Nane Nane Show 2,170,000.00 - 2,170,000.00 2,169,600.00 400.00 11. Training of Farmer Facilitators (FFs) - 170,000.00 3,695,000.00 3,729,000.00 136,000.00 12 Miniprojects Participatory Farmer Groups (PFGs) of - 2,800,000.00 - 2,800,000.00 - 13 Season long training of 180 Participatory Farmer Group 2009/10 90,000,000.00 - 90,000,000.00 80,500,000.00 9,500,000.00 14 Construction of market Shed at Hamkoko 12,000,000.00 4,000,000.00 12,000,000.00 16,000,000.00 - 15 Constuction of crop storage facility at Murutanga 28,000,000.00 - 28,000,000.00 28,000,000.00 - 16 Rehabilitation of rural feeder roads at Muhande 18,844,000.00 - 18,844,000.00 18,844,000.00 - 17 Rehabilitation of rural feeder roads at Bukungu 16,000,000.00 - 16,000,000.00 16,000,000.00 - 18 Construction of 3 shallow wells at Bukungu 12,000,000.00 - 12,000,000.00 12,000,000.00 - 19 Construction of market Shed at Bukiko 28,000,000.00 - 28,000,000.00 28,000,000.00 - 20 Construction of market Shed at Nyang’ombe 28,000,000.00 - 28,000,000.00 28,000,000.00 - 21 Construction of market Shed at Chamuhunda 28,000,000.00 - 28,000,000.00 28,000,000.00 - 22 Construction of market Shed at Kaseni 28,000,000.00 - 28,000,000.00 28,000,000.00 - 23 Construction of market Shed at Igongo 28,000,000.00 - 28,000,000.00 28,000,000.00 - 24 Construction of market Shed at Namilembe 28,000,000.00 - 28,000,000.00 28,000,000.00 - 25 Construction of market Shed at Chankamba 16,000,000.00 - 16,000,000.00 16,000,000.00 - 26 Construction of Culvert at Kakerege 5,600,000.00 - 5,600,000.00 5,600,000.00 - 27 Training Ward Training Facilitators (WFTs) 4,515,000.00 - 4,515,000.00 4,515,000.00 - 28 Construction of market shed at Busunda 2 4,000,000.00 - 24,000,000.00 24,000,000.00 - 29 Construction of market shed at Mukunu 28,000,000.00 - 28,000,000.00 28,000,000.00 - 30 Construction of shallow well at Kweru - 3,200,000.00 - 3,200,000.00 - 31 Preparation of 2010/2011 DADP Planning and Implementation 8,220,000.00 - 8,220,000.00 8,220,000.00 - 32 PFG Mini Grants for 2008/2009 whose their business plan are qualified. 32,400,000.00 - 32,400,000.00 - 32,400,000.00 33 Twelve village agricultural technologies projects 31,000,000.00 - 31,000,000.00 - 31,000,000.00 34 Eight village agricultural technologies projects 24,000,000.00 24,000,000.00 - 24,000,000.00 TOTAL AMOUNT 503,399,000.00 68,189,192.00 543,334,000.00 496,610,700.00 114,912,492.00 8 PROJECT OUTPUT/RESULTS FOR COMPLETED PROJECTS Na. PROJECT NAME PLANNED ANNUAAL TARGET YEAR STATUS % OUTPUT Construction of slaughter slab at Bwasa village Completion of one slaughter slab 2007/08 Completed 100 Reduced hire cost of slaughter site from 3,000/= to 500/= per animal slaughtered which was previously paid to a private owner of a killing stone. This has enabled more serving resulting into capacity to pay school fees for secondary school students Construction of market shed at Igongo village Completion of market shed as per new drawings 2008/09 Completed 100 Not operational Construction of market shed at Nkilizya village Completion of market shed as per new drawings 2007/08 Completed 100 20 peoples use the market to sell agricultural products and fish, more than 400 villagers are enjoing service from the market. Rehabilitation of rural feederload for Mwitibi – Kuchara at Bukungu To complete the rehabilitation of the feederload 2008/09 Completed 100 The load serves on average 150 famers every day on transport and transportation of farm products and inputs from farms and to their fields 9 village respectively Construction of 3 shallow wells at Bukungu village To facilitate agriculture irrigation practices and water for livestock 2008/09 Completed 100 Farmers who owns plots near the shallow wells are using the water for irrigating Tomato, Spinach and amaranthus. Livestock keepers are using water for animal drinking. Farmers also use water for domestic use Construction of culvert at kakerege village To facilitate transport and transportation of farm produce 2008/09 Completed 100 The load serves 300 peoples on average a day, lorries are able to carry orrange fruits at farmers homestead. This has increased price of one sack of orrange from 3,000/= to 3,700/= due to reduced labor cost Construction of 7 shallow wells at Kakerege village To facilitate agriculture irrigation practices and water for livestock 2008/09 Completed 100 In average 10 farmers use water for irrigating horticultural crops every day at each shallow well making a total of 80 farmers using water for irrigation per day. Some farmers have managed to pay school fees, construction of residentia houses and accumulation of capital Rehabilitation of rural feederload at To facilitate transport and transportation of farm 2008/09 Completed 100 The area is very potencial for banana production. The feederload have allowed 10 Busiri village produce vehicles to reach areas of production thus increasing the price of banana from 6,000/= to 9,000/= Construction of market shed at Hamkoko village Expansion of market shed to comply with the new drawings 2007/2008 Completed 100 Not operational 11 A SUCCESS STORY FROM A STRATEGIC FARMER THROUGH SHALLOW WELL WATER USE AT KAKEREGE VILLAGE. 1: BACKGROUND INFORMATION. Kakerege is one of the 30 villages implementing DASIP activities in Ukerewe district. The village is in Mumbuga division and Kakerege ward. In the financial year 2008/09 the Village Agricultural Development Plan (VADP) put forward the construction of 7 water shallow wells as a community need purposely for both irrigation of horticultural crops and water for animals as well as domestic purposes. All 7 shallow wells are in place, but the success story has been obtained from Kakerege harmlet where one strategic farmer resides. 2: SITUATION BEFORE WATER SHALLOW WELL CONSTRUCTION The population for kakerege hamlet is 410 as per 2002 census. The population had water constraint in such a way that it was not easy to produce horticultural crops during off season. Apart from the temporal streams, there is no reliable source of irrigation water, thus gardening was limited to rain fed type. 3: SITUATION AFTER THE SHALLOW WELL CONSTRUCTION. Bestina Lusato is a house wife with 7 people in her family. She decided to utilize the shallow well as an opportunity for producing horticultural crops as source of income. . She started by producing African egg plants which didn’t pay much, She decided to switch to Amaranthus, a crop that is more profitable in terms of cost of production, time and management. By using the shallow well, she is capable of producing Amaranthus 12 times a year. Her earnings through sales of vegetable has raised between 25,000/= and 30,000/= per season. This gives her an income of Tshs 360,000/= per year. 4: SUCCESS i) Bestina has four (4) students at secondary school level and she is able to pay school fees. ii) Bestina has accumulated (She wasn’t willing to mention the amount) capital to achieve her objective of opening a tailoring shop iii) Bestina claimed to have controlled eye problems and other nutritional disorder to her people in her family. iv) She has a plan to do the finishing to her house after this harvest Generally she is very happy and she is encouraging others to use the shallow wells for irrigation because there is a room for livelihood improvement. 5: CHALLENGES FACED BY FARMER. i) Insufficient land area for production which is less than 0.25 of an acre. ii) Insect – pest attack to the crops like aphids. iii) Untrained input sellers who fail to give appropriate information regarding the use of inputs purchased 6: STRATEGIES AGAINST EXISTING CHALLENGES. i)To have regular communication with extension officers for extension services. ii)To intensify more on the small land available so that it produces to its capacity and if possible, hire more land. 12 GENERAL IMPLEMENTATION CHALLENGES FOR DASIP ACTIVITIES ™ Some of the completed projects are semi or completely not operational due lack of polical push resulting from clossness to erection time ™ Community contribution of 20% requires etra effort resulting into untimely accomplishing planned activities e.g purchase of power tillers ™ Limited resource funds for monitoring and follow up reduces the suppervision frequence so leading to failure in rectifying mistakes at arly stages. ™ Limited number of extension staff leads to untimely formation of PFGs. RECCOMENDATIONS ™ Vilage leaders should make by laws to renforce villager and other busnessman to sell their commodities in the farmer markets sheds. ™ More funds should be deviced for monitoring and motorcycle allowances to enhence a wide coverage area for the suppervising staff in the district. ™ The LGA should bollow funds from its sources to facilitate the purchase of power tillers. Farmers will be willing to contribute when the tractors are within their reach. 13 14

# Extracted Content UNITED REPUBLIC OF TANZANIA MINISTRY OF REGIONAL ADMINSTRATION AND LOCAL GOVERNMENT UKEREWE DISTRICT COUNCIL DISTRICT AGRICULTURE SECTOR INVESTMENT PROJECT SEMI ANNUAL IMPLEMENTATION REPORT JULY –DECEMBER 2009 Prepared by District Executive Director P.O Box 41 NANSIO/UKEREWE Tel: 028 – 2515021 Fax: 028 -2515021 1 TABLE OF CONTENTS CONTENTS PAGE 1.0 ABREVIATIONS 3 1.0 .INTRODUCTION AND BACKGROUND INFORMATION 4 2.0 PLAN AND IMPLEMENTATION 4 2.1 PLANS 4 2.2 IMPLEMENTATION STATUS 5 3.0 ASSESSMENT OF CRITICAL ASSUMPTIONS 6 4.0 OUTPUT RESULTS 6 5.0 LESSONS LEARNED AND EXPERIENCE GAINED 6 6.0 PROBLEMS AND CHALLENGES 7 7.0 WAY FORWARD 8 8.0 RECOMMENDATIONS 8 9.0 CONCLUSION 9 ANNEX 10.0 IMPLEMENTATION STATUS 10 2.3 FINANCIAL STATUS 12 2 DMEO- District Monitoring and Evaluation Officer DPO- District Project Officer FAPOEL-Family Poverty Elimination. FFS - Farmer Field School. FFs - Farmers Facilitators. MMEM-Mpango wa Maendeleo ya Elimu ya Msingi. MMES - Mpango wa maendeleo ya Elimu ya Sekondari. O&OD -Opportunity and Obstacles for Development. PCU -Project Coordination Unit. PFGs - Participatory Farmers Groups. SCC-Vi- Swedish Cooperative Center. SUA- Sokoine University of Agriculture. . TASAF-Tanzania Social Action Fund. WTFs –Ward Training Facilitators. 3 1.0 INTRODUCTION AND BACKGROUND INFORMATION Ukerewe District Council is among of the six (6) Districts in Mwanza Region that implement District Agriculture Sector Investment Project (DASIP). The project is implemented in 20 wards as well as in 30 villages. Through DASIP the District implements the main components of the project which are community planning for Agriculture Investment, Farmers Capacity Building and rural finance as well as marketing promotion. The project implementation also is aiming to increase food security as well as the income in community level. Accordingly 30 villages that are in DASIP implementation area are still going on to prepare micro projects which are obtained through exercise of Opportunity and Obstacle to Development (O & OD).For the year 2007/08 in Ukerewe District project beneficiaries were 1288 farmers through 48 PFGs from 26 villages with an average of 27 farmers per PFG of which about 49% were women. This quartery implementation report is explaining on the following areas: • Plan and implementation status. • Financial status • Assessment of critical assumption and risks. • Output results (Achievements after implementation of the projects) • Lessons learned and experience gained • Problems and challenges • The way forward • Recommendation • Conclusions of the report. 2. PLANS. 2.1 Community planning and Investment in Agriculture 2.1.1 Completion of the second shallow well at Namagondo village. 2:1:2 Completion of last F/Y 2007/08 microprojects, -Construction of Farmer market centres at Chankamba, Nkilizya, Hamkoko, Chabilungo, Kazilankanda and Nyamanga villages . -Purchase of irrigation water pumps at Busunda and Muhande villages. -Construction of 2 slaughter slabs at both Bwassa and Sambi villages. -Procurement of grain milling machine – Murutanga village. -Construction of 3 shallow wells at Nantare and Kweru villages. -Rehabilitation of feeder road at Nabweko village. 2:1:3 Medium size and rural infrastructure. - Identification of rural feeder roads. -Identification of suitable area for irrigation. 2:2 Planning and implementation Capacity Building. -Identification and formation of PFGs -Training of FFs. -Follow ups of 17 PFGs of poultry keeping and 3PFGs of dairy goat keeping. - Sensitization of the communities on the issue of opening the village DASIP A/c especially for ones which have not opened yet. 4 -Procurement of bicycles. 3. IMPLEMENTATION STATUS 3.1 Community planning and Investment in Agriculture 3.1.1 The second shallow well at Namagondo village is completed. 3.1.2 The 2007/2008 micro projects implementation status are in different stages as follows: -Hamkoko market shed is completed. -Chankamba market shed is already roofed and is going on with other steps. -Nkilizya market shed is in the stage of oversite concrete completion. -Chabilungo market shed is through with tendering process and the contractor is on the way to start soon after procurement process to be finished. -Nyamanga market shed is on the way to do tendering process after having and compromising with the idea of expanding their market size. -Kazilankanda market shed is in final stage of roofing. - Purchase of irrigation water pumps at Busunda 4 have already purchased and distributed to the respective groups. - Purchase of irrigation water pumps at Muhande,is not done yet, instead the village has decided to change their plan due to unsustainable source of water. -Construction of 2 slaughter slabs at both Bwassa and Sambi villages,these are in procurement stage (Tendering process). - Procurement of grain milling machine in Murutanga village,this activity is in procurement stage,and construction of the shed as their contribution of 50% is going on. -Construction of 3 shallow wells at Nantare and Kweru villages,these construction have not constructed yet but Nantare people have insisted to implement shallow well construction. -Rehabilitation of feeder road at Nabweko village is in chamber formation about 2 km. 3:1:3 Medium size and rural infrastructures -Total of 20km were identified/proposed to be rehabilitated. That proposal has already sent to PCU. - The area that identified is the basin of Miyogwezi and is surrounded by 3 villages of Kameya,Busagami and Igongo. 3.2 Planning and implementation Capacity Building -A total of 150 PFGs have been identified and formed in 28 villages. -A total of 26 FFs have undergone training for the first phase,they are expecting to be trained 3 phases. by now are in good position to facilitate the farmers. -For the case of follow ups on 17 PFGs of poultry and 3 dairy goat keeping (Livestock).All PFGs were through on both part of theory and practical training. -A total of 80 bicycles have been procured and distributed to the respective persons who are WTF and FFs. . 5 4. ASSESSMENT OF CRITICAL ASSUMPTIONS AND RISKS o Inadequacy number of Extension staff in the District has a big effect to projects/activities implementation. o Cooperation of village government leaders with the District level in order to achieve the implementation goal. o Carefulness in fund management and other resources. o Reinforcement of regular communication with other Agricultural development stakeholders especially private sectors i.e. FAPOEL,SCC-Vi and all CBOs by recognizing their services and efforts on project implementations. 5. OUTPUT RESULTS o Good Office management/environment due to availability of purchased stationeries. o Availability of water to domesticated animals (livestock), Agricultural and domestic purposes at Namagondo village community, due to completion of two shallow wells. o More than 90% of Livestock PFGs did fine due to close follow ups by respective staffs. o A total of 18 DASIP A/c at village level are already opened which are the most important in project implementation. o Currently WTFs and FFs are in good position to attend all matters associated to FFS as well as extension services concerned.This is because of being facilitated bicycles. o Four groups in Busunda village have fulfilled their goal due to the procurement of irrigation water pumps.Those pumps will help them to increase production especially in their horticultural activities (Tomatoes,Onion,water melons and vegetables). 6. LESSONS LEARNED AND EXPERIENCE GAINED ƒ Cooperation with Government leaders (DC, DED and Councilors) is very important on sensitization of community contribution due to slow implementation of micro projects. The main reason that cause this particular problem is a number of community contribution in other development projects of MMES, MMEM and TASAF with the same weight of not less than 20%. ƒ Through continuous sensitization and regular communication, a large number of community has an access with project (DASIP) especially 30 villages that are within the area of project implementation. ƒ Strategy of transport means facilitation/provision to DPO, DMEO and DTCs has improved their status of making follow ups on the activities of the project. 6 ƒ Motor vehicle transport is still very important, especially during rain season while doing follow ups it becomes hot issue when you are in a team style. ƒ Contractors, who are weak financially, cause high costs in implementation on since they normally lag behind the time. ƒ Generally most of the contractors hesitate to work with the projects that involve the community,this is due to the problem of existence poor contribution. ƒ Participatory sensitization should be a continuous process purposely for speeds up the implementation of community projects/activities. ƒ Close backstopping is needed, especially on the issues related to financial management and procurement procedures since these knowledge or innovation are very important as a whole. ƒ At the end of the season most of the farmers through their FFS have observed a number gaps by comparing technical and local treatment or management and realized that is very possible to improve their production. ƒ Generally livestock enterprises are more expensive compared to crop enterprises as they involve much inputs and livestock house construction as a whole. 7. PROBLEMS AND CHALLENGES • Bicycle transport is still perceived negatively, because of being tiresome, this is due to a very little number of available extension staff in the District whereby under their fewness have to cover long distance in order to serve their respective areas. Some of them serve more than one ward, whereby in a real situation performance cannot be of the required ones. • Fluctuation of prices in fuel and other building materials affects much the budget in projects implementation. • The District being an island, projects are implemented at high costs due to transport costs of materials • Failure to get contractors even though several advertisement of tender has been made to some of the micro projects. • The number of Extension staff (WTF) is continuously decreasing due to various reasons, among them are through compulsory retirement, continuous sickness and others leaving for further training. For instance in academic year 2008/2009 DMEO and one DTC (livestock part) left for further training to join Sokoine University of Agriculture(SUA). Replacement is already in place, but training is needed to enable them on how to account their duties • Unreliability of rainfall of the current season 2008/09 its likely to affect the FFS activities especially on the side of crops. 7 • Budget is not enough especially for the accommodation of Engineers (District civil work Engineer and District water Engineer) to make their continuous close follow ups in the site. 8. THE WAY FORWARD 9 To put extra effort on follow ups so as to increase speed on the implementation of the micro projects funded last FY 2007/08.The purpose is to complete them very quick in order to make PCU to release the funds of FY 2008/09. 9 To make preparations of the PFGs participants to graduate, since the FFS season long training 2007/08 is through. 9 To increase a close supervision and follow ups to the micro projects of which are being implemented i.e. F/Y 2007/08 for the efficient achievement. 9 To make a regular communication with DASIP villages level and other stakeholders who are very important to the project implementation. 9 To continue with completion of group formation in a participatory way to achieve/meet district goal of 180 PFGs in the year season of 2008/09. 9 To continue with preparations of Medium size rural infrastructures projects implementation. i.e. Water control structures and Rural feeder roads. 9 To continue with follow ups on the participants acquired last season training to make sure that the knowledge and skills are useful,for the side of changing their life in income as well as imparting that knowledge to other farmers. 9. RECOMMENDATIONS I. Still there is a need of having motor – vehicle at District level to encounter the transport problem which normally face the District especially during rain season, and when the activity/trip involves a number of staffs. II. Farmers Field Schools (FFS) budget should be increased due to the shooting price of various commodities especially in Agricultural inputs for example pesticides facilities and other chemicals so far as agricultural commodities concerned. III. Ukerewe as an island should be considered to be given extra fund for the boat hiring in order to encounter problem of transport especially in small islands such as Kamasi, Busumba, Kweru and Irugwa. IV. DPOs should be provided an air time fund to encounter communication costs which are being regularly done by them purposely for the facilitation of project issues. V. DPO and DMEO should be involved in some of the DTCs training in order to make access during on making follow ups and monitoring activities wich is also helpful in supervision process. 8 9 VI. Still follow ups to farmers who attended the FFS is needed, this is to make sure the knowledge acquired to be useful to them purposely for production improvement. 10. CONCLUSION Through DASIP, its hoped that the project is going to contribute much on the issue of poverty reduction among farmers to increase production and productivity. Increasing of the number of PFGs from 2 to 6 per village, its an indicator that up to the end of the project in 2012 most farmers will acquire agricultural skills and knowledge. Therefore seriousness should be taken to make follow ups on everything which farmers are being taught that should encourage sustainability and fulfill the objectives. Additionally we must be very careful on the micro projects that are being prepared by villages to make sure that are of good quality. ANNEX I 11.0IMPLEMENTATION STATUS OF MICRO PROJECTS IN SEMI ANNUAL JULY-DECEMBER FY 2008/09 FUNDS 000 REMARKS S/N PLANNED ACTIVTY/PROJECTS IMPLEMENTATION STATUS BUDGET DISBURSEM ENT EXPENDITU RE BALANCE 1 Construction of market center – Chankamba village. The market is already roofed 12,000 12,000 8,807.925 3,192.075 Community contribution of 20% is still going on to be collected. 2 Construction of market center – Chabilungo village. Its on the completion of procurement stage. 12,000 12,000 12,000 - The contractor will start the work soon after procurement procedure to be completed. 3 Construction of market center – Hamkoko village. The market shed is completed 12,000 12,000 11,400.0 600.0 The market will be handled to the village management soon after some correction to be in place. 4 Construction of market center – Kazilankanda village. The market shed is in the final stage of roofing. 12,000 12,000 6,990.0 5,010.0 Community contribution of 20% is going on to be collected. 5 Construction of market center – Nyamanga village. Its on procurement stage. 12,000 12,000 - 12,000.0 Its on procurement stage. 6 Construction of market center – Nkilizya village. The market shed is in the stage of over site concrete completion. 12,000 12,000 - 12,000.0 Community contribution of 20% is going on to be collected and sensitization as well. 7 Purchase of irrigation water pumps – Busunda village. 4 irrigation water pumps are already procured 3,600 3,600 3,600 - Sensitization to the groups should be proceed to utilize the pumps on the proper manner in order to fullfil their target. 8 Purchase of irrigation water pumps – Muhande village. Not yet done. 6,400 6,400 - 6,400.0 The village has proposed to change the project to be construction of 2 shallow wells instead of water pumps procurement. 9 Construction of slaughter slabs Bwassa village The slaughter slab is in Tendering process Advitersement 1,920 1,920 - 1,920.0 Funds have been transferred to their village A/C, 20% contribution is still going on. 10 10 Construction of slaughter slabs – Sambi village The slaughter slab is in Tendering process Advitersement 1,920 1,920 - 1,920.0 They are going on with 20% collection. 11 Purchase of grain milling machine – Murutanga village. Is under procurement process. 1,750 1,750 - 1,750.0 They are at foundation stage as their 50% contribution. 12 Construction of shallow wells - Nantare village. Not yet done. 6,400 5,991.455 - 5,991.455 Assessment team from PCU came and challenged the plan, so they advised to prepare another plan ,but the village has insisted that shallow wells are its their priority. 13 Construction of 1 shallow well – Kweru village. Not yet done. 3,200 3,200 - 3,200.0 Assessment team from PCU came and challenged the plan, so they advised to prepare another plan. 14 Rehabilitation of feeder road – Nabweko village. The rehabilitation is going on, about 2.0km is finished 20,000 20,000 - 20,000.0 The work is going on,though the contractor has not yet requested any amount up to date. 15 Construction of 2 shallow wells at Namagondo village. Construction of all 2 shallow wells are fully completed. 6,400 6,400 6,400 - Water is available for livestock as well as agricultural activities concerned. 11 ANNEX II 12.0 DASIP- SEMI ANNUAL FINANCIAL REPORT 2008/09 DISBURSEMENT S/N ACTIVITY/ PLAN BUDGET 2007/08 BALANCE FROM 2007/08 DISBURSEMENT 2008/09 EXPENDITURE BALANCE 1 Office operation and maintenance cost 1,500,000.00 736,797.50 750,000.00 1,094,500.00 392,297.50 2 Staff field allowances 8,750,000.00 636,000.00 4,375,000.00 3,444,600.00 1,566,400.00 3 DTCs field allowances 1,300,000.00 101,000.00 650,000.00 618,000.00 133,000.00 4 DTCs office operating expenses 1,200,000.00 594,000.00 600,000.00 363,200.00 830,800.00 5 Motorcycle allowances for DMEO and DPO 1,080,000.00 - 900,000.00 900,000.00 - 6 Motorcycle allowances for DTCs 1,080,000.00 - 900,000.00 900,000.00 - 7 Identification/Formation of sixty participatory farmer groups - 400,000.00 - 399,900.00 100.00 8 Training of ward level Facilitators - 359,000.00 - - 359,000.00 12 9 Season long training of 40 participatory farmer groups - 4,572,000.00 - 457,500.00 4,114,500.00 10 Season long training of 40 participatory farmer groups-Transport cost - 1,227,000.00 - 1,170,000.00 57,000.00 11 Procurement of Bicycles - 7,000,000.00 - 7,000,000.00 - 12 Formation of 2008/09 Participatory Farmer Groups (PFGs) 2,300,000.00 - 2,300,000.00 1,800,000.00 500,000.00 13 Training of Farmer Facilitators (FFs) 8,830,000.00 - 8,830,000.00 3,100,000.00 5,730,000.00 14 Season long training of 180 Participatory Farmer Group 90,000,000.00 - 90,000,000.00 24,150,000.00 65,850,000.00 15 Miniprojects of Participatory Farmer Groups (PFGs) 19,200,000.00 - 19,200,000.00 - 19,200,000.00 16 Hamkoko - 4,000,000.00 - - 4,000,000.00 17 Kazilankanda - 16,000,000.00 - - 16,000,000.00 18 Construction of 2 shallow wells- Namagondo - 2,116,811.00 - 2,099,000.00 17,811.00 13 19 Purchase of irrigation water pumps-Muhande - 6,400,000.00 - - 6,400,000.00 20 Construction of slaughter slab-Sambi - 1,920,000.00 - - 1,920,000.00 21 Construction of market centre-Chabilungo - 12,000,000.00 - 12,000,000.00 - 22 Construction of 1 shallow wells-Kweru - 3,200,000.00 - - 3,200,000.00 23 Construction of 2 shallow wells-Nantare - 5,991,455.00 - - 5,991,455.00 TOTAL 135,240,000.00 67,254,063.50 128,505,000.00 59,496,700.00 136,262,363.50 14 Watering and weeding being carried out by group members in the field 15 16

# Extracted Content © IDARA YA USALAMA WA CHAKULA - SEHEMU YA URATIBU MAZAO NA TAHADHARI YA AWALI S. L. P. 2182 DODOMA | JAM JAMHURI YA MUUNGANO WA TANZANIA WIZARA YA KILIMO | TAARIFA YA MWELEKEO WA MVUA ZA VULI, OKTOBA – DESEMBA, 2021 1. UTANGULIZI Mnamo tarehe 2 Septemba 2021, Mamlaka ya Hali ya Hewa Tanzania (TMA) ilitoa utabiri wa mwelekeo wa hali ya unyeshaji mvua katika Msimu wa Vuli kwa maeneo yanayopata mvua za misimu miwili kwa mwaka (bimodal areas). Maeneo hayo yanajumuisha Nyanda za juu Kaskazini-mashariki, Pwani ya Kaskazini ikiwa ni pamoja na visiwa vya Unguja na Pemba, ukanda wa Ziwa Viktoria pamoja na kaskazini mwa mikoa ya Kigoma na Morogoro . Taarifa hii inatoa uchambuzi wa mwelekeo wa msimu wa mvua katika kipindi cha Oktoba – Desemba 2021, ushauri na tahadhari kwa wadau wa sekta ya kilimo ikijikita zaidi kwenye kilimo cha mazao ya chakula katika mustakabali mzima wa menejimenti ya usalama wa chakula kwa Taifa. 2. MWELEKEO WA MSIMU WA MVUA KIPINDI CHA OKTOBA - DESEMBA, 2021 Kielelezo 1: Matarajio ya unyeshaji mvua za Vuli, 2021 kwa Tanzania Taarifa ya TMA ilieleza kuwa katika kipindi cha Oktoba hadi Desemba, 2021 mvua za chini ya wastani hadi wastani zinatarajiwa katika maeneo mengi yanayopata mvua za Vuli. Mvua hizi zinatarajiwa pia kuambatana na vipindi virefu vya ukavu vinavyozidi siku kumi hasa katika miezi ya Oktoba na Novemba. Aidha, mvua hizo zinazotarajiwa kuanza kwa kusuasua kati ya wiki ya tatu na ya nne ya mwezi Oktoba, 2021 na kuisha kati ya wiki ya nne ya mwezi Desemba, 2021 hadi Januari, 2022 zinatarajiwa pia kuwa na mtawanyiko usioridhisha kwa shughuli za kilimo. Hata hivyo, vipindi vifupi vya mvua kubwa pia vinatarajiwa kujitokeza katika baadhi ya maeneo hasa katika mwezi Desemba. Taarifa ya kina ya hali ya mwenendo wa mvua inayotarajiwa kwa kila Wilaya zinazopata mvua za Vuli itafuata katika kipengele namba 4. 3. ATHARI NA USHAURI WA JUMLA KWENYE SHUGHULI ZA KILIMO CHA MAZAO NA USALAMA WA CHAKULA: Matokeo yanayotarajiwa:  Upungufu wa unyevunyevu katika udongo unatarajiwa kujitokeza katika maeneo mengi na hivyo kuathiri ukuaji wa mazao.  Mlipuko wa visumbufu vya mazao kama vile viwavi jeshi vamizi (FAW) na magonjwa yasiyo ya fangasi vinatarajiwa kuongezeka. Athari:  Ukuaji hafifu wa mazao utakaosababisha kupungua kwa uzalishaji wa mazao ya chakula katika msimu wa Vuli na hivyo kupunguza mchango wa mazao ya Vuli katika chakula kwa mwaka wa chakula 2021/2022. Ushauri: Wakulima wanashauriwa yafuatayo:  Maandalizi ya mashamba yafanyike mapema ili upandaji wa mazao ufanyike mapema mara tu mvua zitakapoanza;  Watumie mbinu na teknolojia za kilimo himilivu ikiwa ni pamoja na kilimo hifadhi, mbinu za uvunaji na uhifadhi wa maji kama matumizi ya majaruba, vilindi vidogo, kilimo cha matuta, umwagilaji kwa njia ya matone, na kutumia matandazo kulingana na maeneo yao;  Wapande mazao yanayostahimili ukame kama vile muhogo, viazi vitamu, mtama, mbaazi na uwele;  Watumie mbegu zinazokomaa kwa muda mfupi;  Kutumia maji vizuri kwenye skimu za umwagiliaji, ikiwa ni pamoja na kufanya kilimo shadidi cha mpunga na kutumia umwagiliaji wa matone kwenye mazao ya bustani;  Kuwekeza zaidi kwenye kilimo cha mboga mboga, ufugaji wa wanyama wadogo wadogo na ndege (kama vile kuku na sungura) na shughuli nyingine za kujiongezea chakula na kipato katika ngazi ya kaya; na  Wakulima wanashauriwa kuwasiliana kwa karibu na kufuata ushauri wa maafisa ugani walioko katika maeneo yao. Maafisa ugani: -Wanakumbushwa kutoa huduma za ugani kwa kuzingatia Mwongozo wa Kilimo Kinachostahimili Mabadiliko ya Tabianchi (Climate – Smart Agriculture Guideline 2017) kwa kilimo himilivu. -Waendelee kushirikiana na vituo vya utafiti vilivyopo kwenye maeneo yao ili kuendelea kupata na kusambaza teknolojia za uzalishaji wa mazao shambani. -Kufuatilia kwa karibu taarifa za mara kwa mara (Updates) kutoka Mamlaka ya Hali ya Hewa nchini ili kuwa na wigo mpana wa kuwashauri wakulima kuhusiana na hali ya hewa na kilimo kwa wakati. Wadau wa pembejeo: Wanashauriwa kuhakikisha upatikanaji wa pembejeo bora kwa wakulima kwa wakati. Wafanyabiashara wa mazao ya chakula: Wanashauriwa kufuatilia mwenendo wa hali ya upatikanaji wa chakula na bei ili kuendana na fursa zitakazojitokeza katika biashara ya mazao ya chakula ikiwemo kununua mazao kutoka maeneo yenye uzalishaji wa ziada kwa msimu wa kilimo 2020/2021 na kuuza kwenye maeneo yenye dalili ya upungufu wa uzalishaji. Jamii kwa jumla: Inasisitizwa kutumia chakula kilichopo sasa kwa uangalifu, kuhifadhi chakula cha kutosha katika ngazi ya familia, na kuepuka matumizi mabaya ya chakula au yasiyokuwa ya lazima kama vile utengenezaji wa pombe na matumizi ya chakula katika sherehe zisizo za lazima. Aidha, inashauriwa wananchi wabadilike, waachane na ulaji wa mazoea wa aina fulani za vyakula. Aina ya mazao yanayoshauriwa kulimwa katika kipindi hiki cha mvua za Vuli, 2021 kwa Mikoa husika Mikoa ya Kagera, Geita na kaskazini mwa mkoa wa Kigoma Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani Mikoa ya Mara, Mwanza, Simiyu na Shinyanga Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani Mikoa ya Tanga, Morogoro Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani Mikoa ya Dar es Salaam na Pwani Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani Mikoa ya Kilimanjaro, Manyara na Arusha Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani 4. TAARIFA YA KINA YA HALI YA MWENENDO WA MVUA ZA VULI INAYOTARAJIWA KATIKA NGAZI YA WILAYA (UTABIRI MAHSUSI): Katika utabiri wa mvua za Vuli kwa mwaka wa 2021, Mamlaka ya Hali ya Hewa iliweza pia kuandaa utabiri wa kina kwa ngazi ya Wilaya katika maeneo yanayopata mvua hizo. Katika utabiri huo imeonekana kuwa Wilaya nyingi zinazopata mvua za Vuli zinatarajiwa kuwa za wastani hadi chini ya wastani kama inavyoainishwa katika Jedwali Na 1 hadi 13 hapa chini. Jedwali 1: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Mkoa wa Mara Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Musoma Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Butiama Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 390 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Bunda Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 370 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Rorya Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 370 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Tarime Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 390 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Serengeti Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 350 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 2: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Mkoa wa Mwanza Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Nyamagana Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 360 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Kwimba Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 360 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Magu Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 360 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Sengerema Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 430 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Ukerewe Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 460 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Ilemela Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 360 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Misungwi Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 400 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 3: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Mkoa wa Simiyu Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Bariadi Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 260 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Busega Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 260 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Itilima Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 260 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Maswa Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 240 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Meatu Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 210 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 4: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Kaskazini mwa mwa Mkoa wa Kigoma Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Kakonko Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 380 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Kibondo Wiki ya tatu, Oktoba 2021 Januari, 2022 Wastani hadi chini ya wastani maeneo kaskazini na Chini ya wastani hadi wasatni maeneo ya kusini . (≤ 380 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 5: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Mkoa wa Shinyanga Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Shinyanga Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Kishapu Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 300 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Kahama Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani maeneo kaskazini na wastani hadi chini ya wastani maeneo ya kusini. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mtama, Uwele, Muhogo, Kunde, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 6: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Mkoa wa Geita Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Bukombe Wiki ya tatu, Oktoba 2021 Januari, 2022 Wastani. hadi chini ya wastani (≤ 380 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Chato Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 440 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Geita Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 410 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Mbogwe Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 410 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Nyang’wale Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 380 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 7: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Ziwa Viktoria-Mkoa wa Kagera Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Bukoba Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 410 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Karagwe Wiki ya tatu, Oktoba 2021 Januari, 2022 Wastani hadi chini ya wastani. (≤ 380 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Kyerwa Wiki ya tatu, Oktoba 2021 Januari, 2022 Wastani hadi chini ya wastani. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Misenyi Wiki ya tatu, Oktoba 2021 Januari, 2022 Wastani hadi chini ya Wastani maeneo ya magharibi na chini ya wastani hadi wastani maeneo ya mashariki. (≤ 410 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Muleba Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 410 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Ngara Wiki ya tatu, Oktoba 2021 Januari, 2022 Wastani hadi Chini ya wastani. (≤ 360 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Biharamulo Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya w astani hadi wastani. (≤ 380 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Maharage, Kunde, Muhogo, Viazi vitamu na mazao ya bustani na mazao mengine yanayofaa Jedwali 8: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Pwani ya Kaskazini- Mkoa wa Pwani Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Bagamoyo Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Rufiji Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Mkuranga Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Kisarawe Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Kibaha Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Mafia Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 170 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Kibiti Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 320 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Jedwali 9: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Pwani ya Kaskazini- Mkoa wa Dar es salaam Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Ilala Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 300 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Kinondoni Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 300 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Ubungo Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 300 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Temeke Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 310 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Kigamboni Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 310 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Viazi vitamu, Kunde, Mbaazi na mazao ya bustani na mazao mengine yanayofaa Jedwali 10: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Pwani ya Kaskazini- Mkoa wa Morogoro Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Morogoro Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 260 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Gairo Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 240 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Kilosa Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 280 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Mvomero Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 240 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Jedwali 10: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya Pwani ya Kaskazini- Mkoa wa Tanga Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Tanga Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 400 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Pangani Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 390 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Muheza Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 430 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Handeni Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 300 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Kilindi Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 300 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Korogwe Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 260 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Lushoto Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 260 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Mkinga Wiki ya tatu, Oktoba 2021 Januari, 2022 Chini ya wastani hadi wastani. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Muhogo, Mtama, Viazi vitamu, Magimbi, Kunde, Mbaazi, Njegere na mazao ya bustani na mazao mengine yanayofaa Jedwali 11: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya nyanda za juu Kaskzini Mashariki- Mkoa wa Arusha Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Arusha Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 220 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Arumeru Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 270 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Karatu Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 220 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Longido Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 220 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Monduli Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 220 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Ngorongoro Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 220 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Jedwali 12: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya nyanda za juu Kaskzini Mashariki- Mkoa wa Kilimanjaro Wilaya Kuanza kwa mvua Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Siha Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Moshi Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 420 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Mwanga Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 420 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Rombo Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 500 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Hai Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Same Kati ya wiki ya tatu na ya nne Oktoba 2021 Wiki ya nne ya Desemba, 2021 Chini ya wastani hadi wastani. (≤ 340 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Jedwali 13: Matarajio ya mvua za Vuli; Oktoba – Desemba, 2021 katika Kanda ya nyanda za juu Kaskzini Mashariki- Mkoa wa Manyara Wilaya Kuanza na Kuisha kwa mvua Matarajio ya jumla ya Mvua Mtawanyiko wa mvua Mazao yanayo shauriwa kupandwa / kulimwa Babati Mvua zinatarajiwa kuanza kwa kusuasua katika maeneo mengi kutokana na kuwepo kwa vipindi virefu vya ukosefu wa mvua (siku 10 au Zaidi) Chini ya wastani hadi wastani. (≤ 230 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Hanang Mvua zinatarajiwa kuanza kwa kusuasua katika maeneo mengi kutokana na kuwepo kwa vipindi virefu vya ukosefu wa mvua (siku 10 au Zaidi) Chini ya wastani hadi wastani. (≤ 200 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Kiteto Mvua zinatarajiwa kuanza kwa kusuasua katika maeneo mengi kutokana na kuwepo kwa vipindi virefu vya ukosefu wa mvua (siku 10 au Zaidi) Chini ya wastani hadi wastani. (≤ 420 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Mbulu Mvua zinatarajiwa kuanza kwa kusuasua katika maeneo mengi kutokana na kuwepo kwa vipindi virefu vya ukosefu wa mvua (siku 10 au Zaidi) Chini ya wastani hadi wastani. (≤ 230 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa Simanjiro Mvua zinatarajiwa kuanza kwa kusuasua katika maeneo mengi kutokana na kuwepo kwa vipindi virefu vya ukosefu wa mvua (siku 10 au Zaidi) Chini ya wastani hadi wastani. (≤ 200 mm). Kiwango cha juu kinatarajiwa mwezi Desemba 2021 Mbaazi, Muhogo, Viazi vitamu, Maharage, Kunde na mazao ya bustani na mazao mengine yanayofaa NB: Kulingana na maeneo husika, wakulima washauriwe kutumia mbinu za kilimo himilivu zilizoainishwa kwenye taarifa hii. ANGALIZO: Taarifa hii imejikita zaidi kwenye Mwelekeo wa mvua uliotolewa na TMA. Matarajio hayo yamezingatia zaidi kipindi cha msimu (miezi mitatu) na hali ya mvua katika maeneo makubwa. Wakulima wanashauriwa pia kufuatilia taarifa za utabiri wa muda wa kati na mfupi, ambao hutolewa na Mamlaka ya hali ya Hewa Tanzania kwa kila siku (saa 24), siku kumi, pamoja na mwezi mmoja. Lakini pia kufuatilia taarifa za tahadhari zinazotolewa kila mara inapotokea hali hatarishi kwa kilimo na ustawi wa jamii kwa ujumla wake, na taarifa za mrejeo (updates) kila inapobidi kutolewa.

# Extracted Content 1 s = United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section WEEKLY MARKET BULLETIN 26 – 30 OCTOBER 2020 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Table 2: Average weekly prices of maize grain (TZS/100 kg bag) Commodity Previous week (Oct 12-16) Last week (Oct 19-23) Current week (Oct 26-30) % Change last vs current Maize 58,000 58,000 57,000 ▼2 Rice 139,600 141,500 142,300 ▲1 Dry beans 202,000 202,900 197,600 ▼3 Sorghum 89,000 86,200 95,100 ▲10 Round potatoes 75,500 71,000 70,100 ▼1 Market Previous week (Oct 12-16) Last week (Oct 19-23 Current week (Oct 26-30) % Change last vs current Arusha (Urban) 51,500 51,500 51,000 ▼1 Bukoba 62,500 63,500 62,500 ▼2 Dar es Salaam - Ilala 60,500 62,000 57,000 ▼8 Dar es Salaam- Kinondoni 72,500 72,500 57,000 ▼21 Dodoma Majengo 62,000 61,500 64,000 ▲4 Dodoma-Kibaigwa 61,000 53,500 56,500 ▲6 Mpanda 51,000 52,500 52,500 ►0 Musoma 60,000 67,500 67,500 ►0 Babati 64,000 64,000 64,000 ►0 Morogoro 55,500 55,500 57,800 ▲4 Mtwara DC 58,000 57,000 57,000 ►0 Tabora 51,500 51,000 51,500 ►0 Tanga/Mgandini 62,500 60,700 60,600 ▼0 Key Messages ✓ On weekly basis, the national average wholesale prices for major food crops varied as follows; Prices for sorghum increased by 10%, and rice by 2%. Prices for dry beans, maize and round potatoes, decreased by 3%, 2% and 1% respectively. ✓ Maize: Prices were highest in Musoma, Dodoma- Majengo and Babati markets, and lowest in Arusha- Urban, Mpanda and Tabora markets. ✓ Rice: Wholesale prices were highest in Dar es salaam- Ilala, Dar es Salaam-Kinondoni and Babati markets and lowest in Mpanda and Musoma markets. ✓ Dry beans: Prices were highest in Mpanda and Tabora markets and lowest in Iringa-urban and Morogoro markets. ✓ Cashew nut: For 2020/21 marketing season, as of 30th October 2020, cashew demanded exceeded supply by about 234,733 MT. 2 Table 3: Average weekly prices of rice (TZS/100kg bag) Table 4: Average weekly prices of dry beans (TZS/100 kg bag) Market Previous week (Oct 05-09) Last week (Oct 12-16) Current week (Oct 19-23) % Change last vs current Arusha- Urban 160,000 165,000 165,000 ►0 Dar es Salaam - Ilala 220,000 220,000 220,000 ►0 Dar es Salaam-Kinondoni 220,000 220,000 220,000 ►0 Dodoma -Majengo 220,000 216,000 217,500 ▲1 Bukoba 207,500 195,000 195,000 ►0 Babati 186,000 186,000 186,000 ►0 Morogoro 182,500 182,500 182,500 ►0 Iringa-Urban 180,000 180,000 185,000 ▲3 Mpanda 210,000 240,000 240,000 ►0 Mtwara DC 210,000 215,000 215,000 ►0 Sumbawanga 225,000 225,000 225,000 ►0 Tabora 205,000 215,000 215,000 ►0 Tanga/Mgandini 160,000 165,000 165,000 ►0 Market Previous week (Oct 12-16) Last week (Oct 19-23) Current week (Oct 26-30) % Change last vs current Arusha - urban 145,000 140,000 135,000 ▼4 Dar es Salaam - Ilala 170,000 170,000 170,000 ►0 Dar es Salaam-Kinondoni 165,000 165,000 165,000 ►0 Dodoma - Majengo 135,000 136,500 142,500 ▲4 Bukoba 134,000 136,000 136,000 ►0 Babati 160,000 160,000 160,000 ►0 Morogoro 130,000 130,000 130,000 ►0 Mpanda 85,000 85,000 85,000 ►0 Mtwara DC 150,000 145,000 145,000 ►0 Sumbawanga 97,500 97,500 97,500 ►0 Musoma 160,000 160,000 160,000 ►0 Iringa Urban 145,000 145,000 145,000 ►0 Tabora 170,000 155,000 155,000 ►0 Tanga/Mgandini 145,000 140,000 135,000 ▼4 3 Table 5: Average weekly prices of sorghum (TZS/100kg) Table 6: Average weekly prices of round potatoes (TZS/100 kg bag) Table 7: Horticulture average prices between (23-28) October 2020 Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kilo) Pineapple (Kilo) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 44,000 74,800 719 609 52,800 96,800 Mombasa 33,293 80,667 550 - 51,333 77,000 Zanzibar 44,250 98,333 767 833 39,167 76,667 Dar es Salaam 21,667 80,000 300 400 36,111 100,000 Morogoro 37,500 73,000 425 - 45,000 83,333 Dodoma 24,000 70,000 267 333 35,000 60,000 Shinyanga 25,000 75,000 533 500 33,333 31,667 Mwanza 18,000 70,833 400 550 16,500 35,000 Mbeya 35,333 64,444 433 1,167 54,333 130,667 Arusha 24,500 80,000 530 475 30,000 75,000 Tanga 25,500 90,000 633 800 40,000 70,000 Lindi 19,000 90,000 300 350 30,000 55,000 Mtwara - 100,000 400 - 30,000 75,000 Source: TAHA, 2020 Market Previous week (Oct 12-16) Last week (Oct 19-23) Current week (Oct 26-30) % Change last vs current Arusha - Urban 65,000 62,500 65,000 ▲4 Dar es Salaam - Kinondoni 80,000 80,000 80,000 ►0 Dar es Salaam - Ilala 105,000 105,000 105,000 ►0 Dodoma - Majengo 47,500 49,000 50,500 ▲3 Dodoma-Kibaigwa 54,500 54,000 54,500 ▲1 Bukoba 140,000 145,000 145,000 ►0 Babati 60,000 60,000 60,000 ►0 Morogoro 95,000 95,000 95,000 ►0 Mtwara DC 80,000 80,000 80,000 ►0 Tabora 145,000 145,000 145,000 ►0 Tanga - Mgandini 90,000 95,000 95,000 ►0 Market Previous week (Oct 12-16) Last week (Oct 19-23) Current week (Oct 26-30) % Change last vs current Arusha- Urban 72,500 72,500 72,500 ►0 Dar es Salaam – Kinondoni 55,000 55,000 57,500 ▲5 Dar es Salaam – Ilala 62,000 62,000 62,000 ►0 Dodoma - Majengo 62,500 63,000 63,500 ▲1 Bukoba 95,000 97,500 97,500 ►0 Babati 67,500 67,500 67,500 ►0 Morogoro 76,000 76,500 76,500 ►0 Musoma 105,000 105,000 105,000 ►0 Mpanda 95,000 95,000 95,000 ►0 Mtwara DC 95,000 67,500 67,500 ►0 Tabora 77,500 72,500 72,500 ►0 Tanga/Mgandini 58,900 58,900 58,900 ►0 4 Table 8: Cashew nut sales for 2020/21 trade season as of 30th October, 2020 Auction Date Union Name Amount Collected (Kgs) Buyers demand (Kgs) Number of buyers Price (TZS/Kg) Amount sold (Kgs) Minimum Maximum 9/10/2020 TANECU 3,109,568 15,952,000 22 2,421 2,707 3,109,568 9/10/2020 MAMCU 6,975,897 31,215,783 25 2,439 2,607 5,526,113 10/10/2020 MWAMBAO 7,143,773 21,247,546 19 2,329 2,470 7,143,773 11/10/2020 RUNALI 2,854,008 16,128,868 21 2,345 2,555 2,854,008 16/10/2020 TANECU 4,109,347 17,883,000 22 2,404 2,459 4,109,347 16/10/2020 MAMCU 9,603,182 32,910,536 21 2,265 2,459 9,602,982 17/10/2020 MWAMBAO 4,755,685 11,994,166 17 2,242 2,395 4,738,653 18/10/2020 RUNALI 7,225,504 16,149,454 17 2,301 2,435 7,225,504 23/10/2020 TANECU 4,726,622 17,333,000 26 2,351 2,415 3,759,000 23/10/2020 MAMCU 8,074,188 39,483,656 22 2,271 2,450 8,074,188 24/10/2020 MWAMBAO 3,180,884 11,051,522 15 1,7501 2,412 3,180,884 25/10/2020 RUNALI 4,989,359 20,974,022 18 1,630 2,410 4,989,359 29/10/2020 TAMCU 2,729,158 11,114,118 16 2,245 2,369 2,729,158 30/10/2020 TANECU 4,761,780 14,293,000 21 2,380 2,459 4,761,780 30/10/2020 MAMCU 5,978,692 34,844,966 21 2,290 2,479 5,978,692 Total 80,217,647 312,575,637 77,783,009 Source: Cashew nut Board of Tanzania, 2020 ✓ For 2020/21 marketing season, as of 30th October 2020 a total of 77,783 MT were sold from a total of 80,218 MT collected with a total demand of 312,576 MT following the auctions conducted. 1 The price for undergrade cashew was TZS. 1,750 and the lowest price for standard grade was TZS. 2,305, while the highest price was TZS. 2,412. 5 Table 9: Fertilizer indicative prices (TZS) as announced on 11th August, 2020 Point of Sale DAP UREA 50 Kg 25 Kg 50 kg 25 Kg Arusha 55,539 28,770 47,757 24,879 Dodoma 54,109 28,054 46,321 24,161 DSM 48,892 25,446 41,462 21,731 Geita 58,496 30,248 50,709 26,355 Iringa 54,746 28,373 46,959 24,480 Kagera 59,378 30,689 52,337 27,168 Katavi 59,614 30,807 52,595 27,297 Kigoma 58,653 30,326 51,546 26,773 Kilimanjaro 54,499 28,250 46,712 24,356 Lindi 54,226 28,113 46,439 24,219 Manyara 56,643 29,321 48,856 25,428 Mara 59,510 30,755 52,481 27,240 Mbeya 56,476 29,238 48,689 25,344 Morogoro 53,211 27,606 45,424 23,712 Mtwara 54,963 28,482 47,176 24,588 Mwanza 58,370 30,185 50,583 26,291 Njombe 55,571 28,785 47,784 24,892 Pwani 51,819 26,909 44,032 23,016 Rukwa 59,439 30,719 52,403 27,202 Ruvuma 57,585 29,793 49,798 25,899 Shinyanga 57,716 29,858 49,929 25,964 Simiyu 57,727 29,864 49,940 25,970 Singida 55,134 28,567 47,347 24,673 Songwe 57,663 29,832 49,876 25,938 Tabora 56,863 29,432 49,594 25,797 Tanga 53,206 27,603 45,419 23,709 Average 55,756 28,878 48,126 25,063 Source: Ministry of Agriculture 6 Table 10: Planting and harvesting time for maximum market price for horticultural crops Commodity The appropriate harvesting time The unappropriated harvesting time Proper time for planting Onions Feb- July July- November Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct - Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon March- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul August - March Dec- Feb Source: TAHA, 2020 Notes: ✓ Unit of measurement: for Food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. They correspond to the percentage change in prices this week compared to last week. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) For further information, contact: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 786 465 121 or +255 754 419 813

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin 11 – 15 April, 2022 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Apr 04 - 08, 2022 Current week Apr 11 - 14, 2022 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 61,100 186,900 184,700 118,300 117,900 168,900 76,700 Previous 61,000 186,600 186,500 116,300 118,300 167,100 79,400 Change ▲0.2% ▲0.2% ▼1.0% ▲1.7% ▼0.3% ▲1.1% ▼3.5% Key Messages This week, wholesale prices for food crops have increased and decreased at different rates compared to price levels last week. Some variations in the average prices of crops were observed across the markets. Prices for sorghum, finger millet, maize and rice has increased by 1.7%, 1.1%, 0.2% and 0.2% respectively while prices for round potato, beans and bulrush millet beans has decreased by an average 3.5% , 1% and 0.3% respectively. Coffee: Total sales were 63,904,458 kilograms with a total value of USD 196 million for the week ended 30th March, 2022. Cocoa: Total sales were 7,772,270 kilograms with a total value of TZS 36.4 billion for the week ended 11th April, 2022. Cashew nut: Until 27th March, 2022, trade season total sale of raw cashew nut was 231,155,915 kilograms with a total value of TZS 489 billion. Fertilizer: Until 14th April, 2022 average price in the world market of DAP increased by 1% while average price in the world market of UREA decreased by 3% compared to last week average price. A weather report released by the meteorological authority shows rainfall (November, 2021-April, 2022) is anticipated to be below average in several areas (Annex 1). This might result in the rise of price of food crops. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 67,600 188,300 185,000 68,500 66,000 137,300 75,800 Previous 61,000 187,000 185,000 64,400 66,000 137,300 93,100 Change ▲9.8% ▲0.7% ►0.0% ▲6.0% ►0.0% ►0.0% ▼22.8% Arusha Current 73,000 210,000 170,000 63,500 69,000 144,000 87,500 Previous 61,000 210,000 175,000 67,500 69,000 138,500 87,500 Change ▲16.4% ►0.0% ▼2.9% ▼6.3% ►0.0% ▲3.8% ►0.0% Dar es Salaam Current 75,300 206,300 230,000 110,000 90,000 170,000 67,200 Previous 72,300 206,300 220,000 110,000 85,000 170,000 66,300 Change ▲4.0% ►0.0% ▲4.3% ►0.0% ▲5.6% ►0.0% ▲1.3% Lindi Current 67,500 197,500 200,000 200,000 NA 200,000 85,000 Previous 75,000 205,000 195,000 175,000 NA 180,000 92,500 Change ▼11.1% ▼3.8% ▲2.5% ▲12.5% ▲10.0% ▼8.8% Morogoro Current 57,500 214,000 205,000 175,000 175,000 175,000 97,500 Previous 57,800 197,500 215,000 175,000 175,000 172,500 98,000 Change ▼0.5% ▲7.7% ▼4.9% ►0.0% ►0.0% ▲1.4% ▼0.5% Tanga Current 58,200 190,000 195,000 100,000 100,000 170,000 90,000 Previous 58,200 190,000 190,000 100,000 110,000 170,000 90,000 Change ►0.0% ►0.0% ▲2.6% ►0.0% ▼10.0% ►0.0% ►0.0% Mtwara Current 55,000 190,000 205,000 NA NA 180,000 NA Previous 55,000 190,000 205,000 NA NA 180,000 NA Change ►0.0% ►0.0% ►0.0% ►0.0% Iringa Current 50,000 195,000 200,000 110,000 NA 150,000 55,000 Previous 49,300 210,000 205,000 110,000 NA 150,000 56,300 Change ▲1.4% ▼7.7% ▼2.5% ►0.0% ►0.0% ▼2.4% Tabora Current 59,000 160,000 190,000 NA NA NA NA Previous NA NA NA NA NA NA NA Change Rukwa Current 55,000 165,000 170,000 NA NA NA 55,000 Previous 55,000 165,000 170,000 NA NA NA 55,000 Change ►0.0% ►0.0% ►0.0% ►0.0% Kigoma Current 53,000 180,000 110,000 100,000 125,000 190,000 90,000 Previous 57,000 175,000 110,000 95,000 125,000 190,000 95,000 Change ▼7.5% ▲2.8% ►0.0% ▲5.0% ►0.0% ►0.0% ▼5.6% Shinyanga Current 57,500 170,000 175,000 95,000 95,000 NA 67,500 Previous 57,500 170,000 175,000 95,000 95,000 NA 67,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 3 Source: Ministry of Investment, Industry and Trade Notes:  Unit of measurement: for food crops are in TZS per 100kg.  The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change in price.  N/A - data not available. Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Mwanza Current 65,000 190,000 200,000 160,000 165,000 180,000 85,000 Previous 65,000 190,000 200,000 160,000 165,000 180,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 57,500 185,000 150,000 130,000 130,000 140,000 74,400 Previous 56,500 171,300 150,000 130,000 130,000 140,000 70,300 Change ▲1.7% ▲7.4% ►0.0% ►0.0% ►0.0% ►0.0% ▲5.5% Mara Current 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Previous 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 66,000 190,000 159,000 90,000 90,000 155,000 105,000 Previous 66,000 190,000 159,000 90,000 90,000 155,000 105,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Njombe Current 58,000 225,000 185,000 NA NA 162,500 48,300 Previous 58,000 220,000 185,000 NA NA 162,500 50,500 Change ►0.0% ▲2.2% ►0.0% ►0.0% ▼4.6% Kilimanjaro Current 67,500 180,000 185,000 120,000 120,000 NA 70,000 Previous 67,500 180,000 185,000 120,000 120,000 NA 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Katavi Current 52,500 165,000 167,500 190,000 NA 190,000 57,500 Previous 60,500 152,500 205,000 190,000 NA 190,000 75,000 Change ▼15.2% ▲7.6% ▼22.4% ►0.0% ►0.0% ▼30.4% 4 Table 3: Average Retail prices (TZS) for Horticulture products for a week of 07tH – 13th April, 2022 Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kg) Pineapple (Kg) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 54,267 222,933 763 682 90,200 36,667 Mombasa 59,547 220,000 660 55,000 110,000 Zanzibar 60,000 280,000 800 1,000 125,000 70,000 Dar es salaam 54,167 330,000 400 400 42,361 126,667 Morogoro 50,000 196,000 500 833 41,667 66,667 Dodoma 35,000 300,000 400 767 33,056 56,667 Shinyanga 35,000 213,333 583 500 29,167 58,333 Mwanza 36,500 285,000 650 1,125 25,000 47,000 Arusha 50,000 222,917 675 575 37,500 52,500 Tanga 48,000 224,000 640 780 42,500 68,000 Lindi 50,000 222,917 675 575 37,500 52,500 Mtwara 46,667 280,000 367 600 100,000 80,000 Mbeya 46,333 241,667 1,000 1,767 51,333 118,667 Average 48,114 249,136 624 800 54,637 72,590 Source: TAHA, 2022 Table 4: Coffee sales (by varieties) for 2021/22 trade season ending 30th March, 2022 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabica 17,443,712 66,463,399 18,813,020 83,588,763 73,283 244,327.36 36,330,015 150,296,489 Hard Arabica 15,760 38,814 665,385 1,446,056 5,400 4,320.00 686,545 1,489,190 Robusta 1,243,294 2,732,643 25,314,488 411,110,040 330,116 475,323.49 26,887,898 44,318,007 Total 18,702,766 69,234,856 44,792,893 126,144,856 408,799 723,970.85 63,904,458 196,108,686 Source: Tanzania Coffee Board, 2022 5 Table 5: Cocoa sales for 2021/22 trade season ending 11th April, 2022 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 5,856,940 1,407,810 507,520 7,772,270 4,754 36,439,814,370 Source: Tanzania Cooperative Development Commission, 2022 Table 6: Cashew nut sales for 2021/22 trade season ending 27th March, 2022 Union Name Amount sold (Kg) Maximum price (TZS/Kg) SG Minimum price (TZS/Kg) SG Maximum price (TZS/Kg) UG Minimum price (TZS/Kg) SG Value of amount sold (TZS) TANECU 56,897,028 2,445 1,615 1,705 1,305 122,053,954,954 MAMCU 69,065,290 2,400 1,350 1,715 1,200 147,755,070,189 LINDI MWAMBAO 19,415,656 2,286 1,700 1,710 1,635 40,156,622,949 RUNALI 46,372,299 2,352 1,400 102,113,318,103 CORECU 13,272,882 2,060 1,900 1,755 1,320 25,015,289,408 TAMCU 25,284,493 2,267 1,700 50,534,029,863 MOFACU 137,080 2,270 1,850 292,763,930 TACACU 711,187 1,700 1,610 1,250 1,200 1,137,802,010 TOTAL SALES 231,155,915 2,445 1,500 1,755 1,200 489,058,851,406 Source: Tanzania Cashewnut Board, 2022 NB: SG: Standard Grade UG: Under Grade Table 7: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 6 Figure 1: Average price trend of UREA fertilizer in the world market as a season of 14th April, 2022 Source: TFRA, 2022 Figure 2: Average price trend of DAP fertilizer in the world market as a season of 14th April, 2022 Source: TFRA, 2022 0 100 200 300 400 500 600 700 800 900 1000 29 Jul 2021 05 Aug 2021 12 Aug 2021 19 Aug 2021 26 Aug 2021 02 Sep 2021 09 Sep 2021 16 Sep 2021 23 Sep 2021 30 Sep 2021 07 Oct 2021 14 Oct 2021 21 Oct 2021 28 Oct 2021 04 Nov 2021 11 Nov 2021 18 Nov 2021 25 Nov 2021 02 Dec 2021 09 Dec 2021 16 Dec 2021 23 Dec 2021 30 Dec 2021 06 Jan 2022 13 Jan 2022 20 Jan 2022 27 Jan 2022 03 Feb 2022 10 Feb 2022 17 Feb 2022 24 Feb 2022 03 Mar 2022 10 Mar 2022 17 Mar 2022 24 Mar 2022 31 Mar 2022 07 Apr 2022 14 Apr 2022 Price (USD/T) Period 0 200 400 600 800 1000 1200 1400 29 Jul 2021 05 Aug 2021 12 Aug 2021 19 Aug 2021 26 Aug 2021 02 Sep 2021 09 Sep 2021 16 Sep 2021 23 Sep 2021 30 Sep 2021 07 Oct 2021 14 Oct 2021 21 Oct 2021 28 Oct 2021 04 Nov 2021 11 Nov 2021 18 Nov 2021 25 Nov 2021 02 Dec 2021 09 Dec 2021 16 Dec 2021 23 Dec 2021 30 Dec 2021 06 Jan 2022 13 Jan 2022 20 Jan 2022 27 Jan 2022 03 Feb 2022 10 Feb 2022 17 Feb 2022 24 Feb 2022 03 Mar 2022 10 Mar 2022 17 Mar 2022 24 Mar 2022 31 Mar 2022 07 Apr 2022 14 Apr 2022 Price (USD/T) Period 7 Important updates  The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders through their mobile phones, how to access services; -  USSD: Dial *152*00# select No. 7 then Na. 2 then follow the instructions  Website: open exts.kilimo.go.tz then select the service Contacts Agricultural Marketing Section, Ministry of Agriculture, P.O. Box 2182, DODOMA. Email: [email protected] 8 Annex: Rainfall Outlook Nov, 2021- April, 2022 Source: Tanzania Meteorological Authority

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin 19 – 22 April, 2022 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Apr 11 - 15, 2022 Current week Apr 19 - 22, 2022 Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 61,900 188,700 184,600 118,300 117,900 169,300 75,000 Previous 61,100 186,900 184,700 118,300 117,900 168,900 76,700 Change ▲1.3% ▲1.0% ▼0.1% ►0.0% ►0.0% ▲0.2% ▼2.3% National Average Key Messages Food crops: Wholesale prices for food crops have increased and decreased at different rates compared to price levels last week, prices for maize, rice and finger millet have increased by an average 1.3%, 1.0% and 0.2% respectively. Prices for round potato and beans has decreased by an average 2.3% and 0.1% respectively while prices sorghum and bulrush millet remained constant. Horticulture: Prices of different markets in the country for horticultural crops changed at different rates. Price for cucumber and onion increased by 5% and 1% respectively while prices for green pepper, water melon, tomato and pineapple decreased by 13%, 4%, 1% and 1% respectively. Coffee: Total sales were 63,904,458 kilograms with a total value of USD 196 million for the week ended 30th March, 2022. Cocoa: Total sales were 7,931,170 kilograms with a total value of TZS 37 billion for the week ended 18th April, 2022. Cashew nut: Until 27th March, 2022, trade season total sale of raw cashew nut was 231,155,915 kilograms with a total value of TZS 489 billion. Fertilizer: Until 21st April, 2022 average price in the world market of UREA increased by 0.2% while average price in the world market of DAP decreased by 0.5% compared to last week average price. A weather report released by the meteorological authority shows rainfall (November, 2021-April, 2022) is anticipated to be below average in several areas (Annex 1). This might result in the rise of price of food crops. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 65,300 188,300 185,000 68,500 66,000 137,300 76,800 Previous 67,600 188,300 185,000 68,500 66,000 137,300 75,800 Change ▼3.5% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲1.3% Arusha Current 76,500 225,000 170,000 63,500 69,000 144,000 87,500 Previous 73,000 210,000 170,000 63,500 69,000 144,000 87,500 Change ▲4.6% ▲6.7% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Dar es Salaam Current 75,300 212,500 232,500 110,000 90,000 170,000 64,400 Previous 75,300 206,300 230,000 110,000 90,000 170,000 67,200 Change ►0.0% ▲2.9% ▲1.1% ►0.0% ►0.0% ►0.0% ▼4.3% Lindi Current 70,000 200,000 200,000 200,000 NA 200,000 70,000 Previous 67,500 197,500 200,000 200,000 NA 200,000 85,000 Change ▲3.6% ▲1.3% ►0.0% ►0.0% ►0.0% ▼21.4% Morogoro Current 63,600 214,000 199,000 175,000 175,000 175,000 97,500 Previous 57,500 214,000 205,000 175,000 175,000 175,000 97,500 Change ▲9.6% ►0.0% ▼3.0% ►0.0% ►0.0% ►0.0% ►0.0% Tanga Current 58,200 190,000 185,000 100,000 100,000 175,000 90,000 Previous 58,200 190,000 195,000 100,000 100,000 170,000 90,000 Change ►0.0% ►0.0% ▼5.4% ►0.0% ►0.0% ▲2.9% ►0.0% Mtwara Current 55,000 190,000 205,000 NA NA 180,000 NA Previous 55,000 190,000 205,000 NA NA 180,000 NA Change ►0.0% ►0.0% ►0.0% ►0.0% Iringa Current 50,000 195,000 200,000 110,000 NA 150,000 55,000 Previous 50,000 195,000 200,000 110,000 NA 150,000 55,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tabora Current 59,000 160,000 190,000 NA NA NA NA Previous 59,000 160,000 190,000 NA NA NA NA Change ►0.0% ►0.0% ►0.0% Rukwa Current 55,000 165,000 170,000 NA NA NA 55,000 Previous 55,000 165,000 170,000 NA NA NA 55,000 Change ►0.0% ►0.0% ►0.0% ►0.0% Kigoma Current 53,000 180,000 110,000 100,000 125,000 190,000 90,000 Previous 53,000 180,000 110,000 100,000 125,000 190,000 90,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Shinyanga Current 57,500 170,000 175,000 95,000 95,000 NA 67,500 Previous 57,500 170,000 175,000 95,000 95,000 NA 67,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 3 Source: Ministry of Investment, Industry and Trade Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change in price. ✓ N/A - data not available. Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Mwanza Current 65,000 190,000 200,000 160,000 165,000 180,000 85,000 Previous 65,000 190,000 200,000 160,000 165,000 180,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 57,500 185,000 150,000 130,000 130,000 140,000 67,500 Previous 57,500 185,000 150,000 130,000 130,000 140,000 74,400 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼10.2% Mara Current 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Previous 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 71,500 200,000 171,000 90,000 90,000 155,000 100,000 Previous 66,000 190,000 159,000 90,000 90,000 155,000 105,000 Change ▲7.7% ▲5.0% ▲7.0% ►0.0% ►0.0% ►0.0% ▼5.0% Njombe Current 58,000 225,000 185,000 NA NA 162,500 48,300 Previous 58,000 225,000 185,000 NA NA 162,500 48,300 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kilimanjaro Current 67,500 180,000 185,000 120,000 120,000 NA 70,000 Previous 67,500 180,000 185,000 120,000 120,000 NA 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Katavi Current 52,500 165,000 167,500 190,000 NA 190,000 57,500 Previous 52,500 165,000 167,500 190,000 NA 190,000 57,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 4 Table 3: Average Retail prices (TZS) for Horticulture products for a week of 15tH – 20th April, 2022 Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kg) Pineapple (Kg) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 61,600 228,800 836 726 83,600 55,000 Mombasa 63,800 247,500 660 55,000 110,000 Dar es salaam 50,000 330,000 400 400 36,667 120,000 Morogoro 52,500 280,000 500 800 43,750 Dodoma 33,333 300,000 400 783 36,667 60,000 Shinyanga 35,000 208,333 500 500 29,167 60,000 Mwanza 33,333 261,111 617 1,017 26,000 45,333 Arusha 50,000 218,750 650 675 37,500 52,500 Tanga 44,500 212,500 675 700 36,250 72,500 Lindi 50,000 218,750 650 675 37,500 52,500 Mtwara 50,000 275,000 400 700 100,000 90,000 Mbeya 45,000 225,000 877 1,767 51,000 118,667 Current average 47,422 250,479 597 795 47,758 76,045 Previous average 48,114 249,136 624 800 54,637 72,590 Change ▼1% ▲1% ▼4% ▼1% ▼13% ▲5% Source: TAHA, 2022 Table 4: Coffee sales (by varieties) for 2021/22 trade season ending 30th March, 2022 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabica 17,443,712 66,463,399 18,813,020 83,588,763 73,283 244,327.36 36,330,015 150,296,489 Hard Arabica 15,760 38,814 665,385 1,446,056 5,400 4,320.00 686,545 1,489,190 Robusta 1,243,294 2,732,643 25,314,488 411,110,040 330,116 475,323.49 26,887,898 44,318,007 Total 18,702,766 69,234,856 44,792,893 126,144,856 408,799 723,970.85 63,904,458 196,108,686 Source: Tanzania Coffee Board, 2022 5 Table 5: Cocoa sales for 2021/22 trade season ending 18th April, 2022 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 5,989,450 1,429,510 512,210 7,931,170 4,753.63 37,157,883,470 Source: Tanzania Cooperative Development Commission, 2022 Table 6: Cashew nut sales for 2021/22 trade season ending 27th March, 2022 Union Name Amount sold (Kg) Maximum price (TZS/Kg) SG Minimum price (TZS/Kg) SG Maximum price (TZS/Kg) UG Minimum price (TZS/Kg) SG Value of amount sold (TZS) TANECU 56,897,028 2,445 1,615 1,705 1,305 122,053,954,954 MAMCU 69,065,290 2,400 1,350 1,715 1,200 147,755,070,189 LINDI MWAMBAO 19,415,656 2,286 1,700 1,710 1,635 40,156,622,949 RUNALI 46,372,299 2,352 1,400 102,113,318,103 CORECU 13,272,882 2,060 1,900 1,755 1,320 25,015,289,408 TAMCU 25,284,493 2,267 1,700 50,534,029,863 MOFACU 137,080 2,270 1,850 292,763,930 TACACU 711,187 1,700 1,610 1,250 1,200 1,137,802,010 TOTAL SALES 231,155,915 2,445 1,500 1,755 1,200 489,058,851,406 Source: Tanzania Cashewnut Board, 2022 NB: SG: Standard Grade UG: Under Grade Table 7: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 6 Figure 1: Average price trend of UREA fertilizer in the world market as a season of 21st April, 2022 Source: TFRA, 2022 Figure 2: Average price trend of DAP fertilizer in the world market as a season of 21st April, 2022 Source: TFRA, 2022 0.0 200.0 400.0 600.0 800.0 1,000.0 1,200.0 1,400.0 06 May 2021 20 May 2021 03 Jun 2021 17 Jun 2021 01 Jul 2021 15 Jul 2021 29 Jul 2021 12 Aug 2021 26 Aug 2021 09 Sep 2021 23 Sep 2021 07 Oct 2021 21 Oct 2021 04 Nov 2021 18 Nov 2021 02 Dec 2021 16 Dec 2021 30 Dec 2021 13 Jan 2022 27 Jan 2022 10 Feb 2022 24 Feb 2022 10 Mar 2022 24 Mar 2022 07 Apr 2022 21 Apr 2022 Price (USD/Ton) Period 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1,000.0 06 May 2021 20 May 2021 03 Jun 2021 17 Jun 2021 01 Jul 2021 15 Jul 2021 29 Jul 2021 12 Aug 2021 26 Aug 2021 09 Sep 2021 23 Sep 2021 07 Oct 2021 21 Oct 2021 04 Nov 2021 18 Nov 2021 02 Dec 2021 16 Dec 2021 30 Dec 2021 13 Jan 2022 27 Jan 2022 10 Feb 2022 24 Feb 2022 10 Mar 2022 24 Mar 2022 07 Apr 2022 21 Apr 2022 Price (USD/Ton) Period 7 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders through their mobile phones, how to access services; - ❖ USSD: Dial *152*00# select No. 7 then Na. 2 then follow the instructions ❖ Website: open exts.kilimo.go.tz then select the service Contacts Agricultural Marketing Section, Ministry of Agriculture, P.O. Box 2182, DODOMA. Email: [email protected] 8 Annex: Rainfall Outlook Nov, 2021- April, 2022 Source: Tanzania Meteorological Authority

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin 25 – 29 April, 2022 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Apr 19 - 22, 2022 Current week Apr 25 - 29, 2022 Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 65,600 188,600 185,200 119,000 119,400 170,300 72,800 Previous 61,900 188,700 184,600 118,300 117,900 169,300 75,000 Change ▲5.6% ▼0.1% ▲0.3% ▲0.6% ▲1.3% ▲0.6% ▼3.0% National Average Key Messages Food crops: Wholesale prices for food crops have increased and decreased at different rates compared to price levels last week, prices for maize, bulrush millet, sorghum, finger millet, beans, have increased by an average 5.6%, 1.3%, 0.6%, 0.6%, 0.3% respectively. Prices for round potato and rice as decreased by an average 3.0% and 0.1% respectively Horticulture: Prices of different markets in the country for horticultural crops changed at different rates. Price for cucumber and onion increased by 5% and 1% respectively while prices for green pepper, water melon, tomato and pineapple decreased by 13%, 4%, 1% and 1% respectively. Coffee: Total sales were 63,904,458 kilograms with a total value of USD 196 million for the week ended 30th March, 2022. Cocoa: Total sales were 8, 125,420 kilograms with a total value of TZS 38 billion for the week ended 25th April, 2022. Cashew nut: Until 27th March, 2022, trade season total sale of raw cashew nut was 231,155,915 kilograms with a total value of TZS 489 billion. Fertilizer: Until 28th April, 2022 average price in the world market of UREA decreased by 0.1% compared to last week average price, while average price in the world market of DAP decreased by 0.04% compared to last week. A weather report released by the meteorological authority shows rainfall (November, 2021-April, 2022) is anticipated to be below average in several areas (Annex 1). This might result in the rise of price of food crops. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 66,500 190,100 185,600 68,600 66,000 141,800 77,000 Previous 65,300 188,300 185,000 68,500 66,000 137,300 76,800 Change ▲1.8% ▲0.9% ▲0.3% ▲0.1% ►0.0% ▲3.2% ▲0.3% Arusha Current 76,500 225,000 170,000 63,500 69,000 139,500 87,500 Previous 76,500 225,000 170,000 63,500 69,000 144,000 87,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼3.2% ►0.0% Dar es Salaam Current 82,800 212,500 237,500 115,000 102,500 185,000 57,600 Previous 75,300 212,500 232,500 110,000 90,000 170,000 64,400 Change ▲9.1% ►0.0% ▲2.1% ▲4.3% ▲12.2% ▲8.1% ▼11.8% Lindi Current 70,000 200,000 200,000 200,000 NA 200,000 70,000 Previous 70,000 200,000 200,000 200,000 NA 200,000 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Morogoro Current 64,000 199,000 195,000 175,000 175,000 172,500 95,000 Previous 63,600 214,000 199,000 175,000 175,000 175,000 97,500 Change ▲0.6% ▼7.5% ▼2.1% ►0.0% ►0.0% ▼1.4% ▼2.6% Tanga Current 64,500 205,000 185,000 100,000 100,000 175,000 65,000 Previous 58,200 190,000 185,000 100,000 100,000 175,000 90,000 Change ▲9.8% ▲7.3% ►0.0% ►0.0% ►0.0% ►0.0% ▼38.5% Mtwara Current 60,000 192,500 191,300 NA NA 180,000 NA Previous 55,000 190,000 205,000 NA NA 180,000 NA Change ▲8.3% ▲1.3% ▼7.2% ►0.0% Iringa Current 61,000 175,000 195,000 110,000 NA 150,000 55,000 Previous 50,000 195,000 200,000 110,000 NA 150,000 55,000 Change ▲18.0% ▼11.4% ▼2.6% ►0.0% ►0.0% ►0.0% Tabora Current 59,000 167,500 190,000 NA NA NA NA Previous 59,000 160,000 190,000 NA NA NA NA Change ►0.0% ▲4.5% ►0.0% Rukwa Current 55,000 165,000 170,000 NA NA NA 55,000 Previous 55,000 165,000 170,000 NA NA NA 55,000 Change ►0.0% ►0.0% ►0.0% ►0.0% Kigoma Current 58,400 185,000 138,800 105,000 122,500 182,500 87,500 Previous 53,000 180,000 110,000 100,000 125,000 190,000 90,000 Change ▲9.2% ▲2.7% ▲20.7% ▲4.8% ▼2.0% ▼4.1% ▼2.9% Shinyanga Current 57,500 170,000 175,000 95,000 95,000 NA 67,500 Previous 57,500 170,000 175,000 95,000 95,000 NA 67,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 3 Source: Ministry of Investment, Industry and Trade Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change in price. ✓ N/A - data not available. Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Mwanza Current 65,000 190,000 200,000 160,000 165,000 180,000 85,000 Previous 65,000 190,000 200,000 160,000 165,000 180,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 67,500 187,500 155,000 130,000 137,500 150,000 67,500 Previous 57,500 185,000 150,000 130,000 130,000 140,000 67,500 Change ▲14.8% ▲1.3% ▲3.2% ►0.0% ▲5.5% ▲6.7% ►0.0% Mara Current 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Previous 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 81,000 200,000 165,000 90,000 90,000 155,000 100,000 Previous 71,500 200,000 171,000 90,000 90,000 155,000 100,000 Change ▲11.7% ►0.0% ▼3.6% ►0.0% ►0.0% ►0.0% ►0.0% Njombe Current 58,000 225,000 185,000 NA NA 162,500 48,300 Previous 58,000 225,000 185,000 NA NA 162,500 48,300 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kilimanjaro Current 76,300 180,000 185,000 120,000 120,000 NA 70,000 Previous 67,500 180,000 185,000 120,000 120,000 NA 70,000 Change ▲11.5% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Katavi Current 58,800 165,000 167,500 190,000 NA 190,000 57,500 Previous 52,500 165,000 167,500 190,000 NA 190,000 57,500 Change ▲10.7% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 4 Table 3: Average Retail prices (TZS) for Horticulture products for a week of 15tH – 20th April, 2022 Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kg) Pineapple (Kg) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 61,600 228,800 836 726 83,600 55,000 Mombasa 63,800 247,500 660 55,000 110,000 Dar es salaam 50,000 330,000 400 400 36,667 120,000 Morogoro 52,500 280,000 500 800 43,750 Dodoma 33,333 300,000 400 783 36,667 60,000 Shinyanga 35,000 208,333 500 500 29,167 60,000 Mwanza 33,333 261,111 617 1,017 26,000 45,333 Arusha 50,000 218,750 650 675 37,500 52,500 Tanga 44,500 212,500 675 700 36,250 72,500 Lindi 50,000 218,750 650 675 37,500 52,500 Mtwara 50,000 275,000 400 700 100,000 90,000 Mbeya 45,000 225,000 877 1,767 51,000 118,667 Current average 47,422 250,479 597 795 47,758 76,045 Previous average 48,114 249,136 624 800 54,637 72,590 Change ▼1% ▲1% ▼4% ▼1% ▼13% ▲5% Source: TAHA, 2022 Table 4: Coffee sales (by varieties) for 2021/22 trade season ending 30th March, 2022 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabica 17,443,712 66,463,399 18,813,020 83,588,763 73,283 244,327.36 36,330,015 150,296,489 Hard Arabica 15,760 38,814 665,385 1,446,056 5,400 4,320.00 686,545 1,489,190 Robusta 1,243,294 2,732,643 25,314,488 411,110,040 330,116 475,323.49 26,887,898 44,318,007 Total 18,702,766 69,234,856 44,792,893 126,144,856 408,799 723,970.85 63,904,458 196,108,686 Source: Tanzania Coffee Board, 2022 5 Table 5: Cocoa sales for 2021/22 trade season ending 25th April, 2022 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 6,157,800 1,454,710 512,910 8,125,420 4,744.71 38,028,123,470 Source: Tanzania Cooperative Development Commission, 2022 Table 6: Cashew nut sales for 2021/22 trade season ending 27th March, 2022 Union Name Amount sold (Kg) Maximum price (TZS/Kg) SG Minimum price (TZS/Kg) SG Maximum price (TZS/Kg) UG Minimum price (TZS/Kg) SG Value of amount sold (TZS) TANECU 56,897,028 2,445 1,615 1,705 1,305 122,053,954,954 MAMCU 69,065,290 2,400 1,350 1,715 1,200 147,755,070,189 LINDI MWAMBAO 19,415,656 2,286 1,700 1,710 1,635 40,156,622,949 RUNALI 46,372,299 2,352 1,400 102,113,318,103 CORECU 13,272,882 2,060 1,900 1,755 1,320 25,015,289,408 TAMCU 25,284,493 2,267 1,700 50,534,029,863 MOFACU 137,080 2,270 1,850 292,763,930 TACACU 711,187 1,700 1,610 1,250 1,200 1,137,802,010 TOTAL SALES 231,155,915 2,445 1,500 1,755 1,200 489,058,851,406 Source: Tanzania Cashewnut Board, 2022 NB: SG: Standard Grade UG: Under GradeTable 7: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 6 Figure 1: Average price trend of UREA fertilizer in the world market as a season of 28st April, 2022 Source: TFRA, 2022 Figure 2: Average price trend of DAP fertilizer in the world market as a season of 28st April, 2022 Source: TFRA, 2022 0 100 200 300 400 500 600 700 80022 Jul 202105 Aug 202119 Aug 202102 Sep 202116 Sep 202130 Sep 202114 Oct 202128 Oct 202111 Nov 202125 Nov 202109 Dec 202123 Dec 202106 Jan 202220 Jan 202203 Feb 202217 Feb 202203 Mar 202217 Mar 202231 Mar 202214 Apr 202228 Apr 2022 Price (USD/T) Period 0 200 400 600 800 100008 Jul 202122 Jul 202105 Aug 202119 Aug 202102 Sep 202116 Sep 202130 Sep 202114 Oct 202128 Oct 202111 Nov 202125 Nov 202109 Dec 202123 Dec 202106 Jan 202220 Jan 202203 Feb 202217 Feb 202203 Mar 202217 Mar 202231 Mar 202214 Apr 202228 Apr 2022 Price (USD/T) Period 7 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders through their mobile phones, how to access services; - ❖ USSD: Dial *152*00# select No. 7 then Na. 2 then follow the instructions ❖ Website: open exts.kilimo.go.tz then select the service Contacts Agricultural Marketing Section, Ministry of Agriculture, P.O. Box 2182, DODOMA. Email: [email protected] 8 Annex: Rainfall Outlook Nov, 2021- April, 2022 Source: Tanzania Meteorological Authority

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin Dec 27-31, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Dec 20-24 Current week Dec 27-31 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 65,300 179,200 183,900 114,600 110,900 166,500 76,000 Previous 59,600 168,300 184,500 114,300 123,700 166,000 72,000 Change ▲8.7% ▲6.1% ▼0.3% ▲0.3% ▼11.5% ▲0.3% ▲5.3% Key Messages This week, wholesale prices for food crops have increased slightly compared to price levels last week (table 1). Some variations in the prices of crops were observed across the markets (Table2). Prices for maize, rice, round potatoes, finger millet and sorghum rose by an average of 8.7%, 6.1%, 5.3%, 0.3%, and 0.3% respectively, On the other hand, prices of brush millet and beans decreased by 11.5% and 0.3% respectively. Coffee: Total sales were 50,583,600 kilograms with a total value of USD 143 million for the week ended 13th December, 2021. Cocoa: Total sales were 6,367,830 kilograms with a total value of TZS 30, billion for the week ended 29th December, 2021. Cashew nut: Until 24th December, 2021, trade season total sale of raw cashew nut was 220,276,691 kilograms with a total value of TZS 470 billion. Fertilizer: Until 23rd December, 2021 average price trend of Urea and DAP in the world market decreased by 8.6% and 0.3% respectively, compared to last week average price (figure 1 and figure 2) A weather report released by the meteorological authority shows rainfall (November, 2021-April, 2022) is anticipated to be below average in several areas (Annex 1). This might result in the rise of price of food crops. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 59,700 197,500 198,800 56,600 54,300 140,000 63,600 Previous 57,400 168,300 213,800 56,800 54,300 140,000 55,400 Change ▲3.9% ▲14.8% ▼7.5% ▼0.4% ►0.0% ►0.0% ▲12.9% Arusha Current 65,500 205,000 157,500 59,000 72,500 129,000 72,500 Previous 64,800 202,500 157,500 59,000 72,500 129,000 72,500 Change ▲1.1% ▲1.2% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Dar es Salaam Current 64,000 185,000 220,000 65,000 72,500 160,000 78,100 Previous 64,000 180,000 220,000 65,000 72,500 160,000 62,500 Change ►0.0% ▲2.7% ►0.0% ►0.0% ►0.0% ►0.0% ▲20.0% Lindi Current 90,000 177,500 225,000 175,000 180,000 215,000 100,000 Previous 95,000 175,000 225,000 175,000 240,000 227,500 100,000 Change ▼5.6% ▲1.4% ►0.0% ►0.0% ▼33.3% ▼5.8% ►0.0% Morogoro Current 64,500 177,500 187,500 150,000 150,000 155,000 81,000 Previous 60,500 172,500 190,000 150,000 150,000 157,500 80,500 Change ▲6.2% ▲2.8% ▼1.3% ►0.0% ►0.0% ▼1.6% ▲0.6% Tanga Current 68,300 165,000 177,500 95,000 80,000 170,000 80,000 Previous 66,700 167,500 177,500 95,000 80,000 170,000 70,000 Change ▲2.3% ▼1.5% ►0.0% ►0.0% ►0.0% ►0.0% ▲12.5% Mtwara Current 68,000 170,000 185,000 NA NA 180,000 75,000 Previous 45,000 170,000 177,500 120,000 NA 180,000 70,000 Change ▲33.8% ►0.0% ▲4.1% ►0.0% ▲6.7% Iringa Current 57,500 180,000 160,000 110,000 NA 150,000 75,000 Previous 57,500 177,500 160,000 110,000 NA 150,000 60,000 Change ►0.0% ▲1.4% ►0.0% ►0.0% ►0.0% ▲20.0% Ruvuma Current 53,800 180,000 162,500 NA NA NA 75,000 Previous 52,500 180,000 162,500 NA NA NA 75,000 Change ▲2.4% ►0.0% ►0.0% ►0.0% 3 Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change in price. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Mwanza Current 78,500 205,000 220,000 185,000 200,000 200,000 100,000 Previous 74,000 207,500 225,000 185,000 200,000 200,000 90,000 Change ▲5.7% ▼1.2% ▼2.3% ►0.0% ►0.0% ►0.0% ▲10.0% Kagera Current 70,000 165,000 125,000 100,000 110,000 165,000 64,700 Previous 70,000 162,500 125,000 100,000 110,000 165,000 57,500 Change ►0.0% ▲1.5% ►0.0% ►0.0% ►0.0% ►0.0% ▲11.1% Manyara Current 62,500 175,000 159,000 70,000 70,000 150,000 85,000 Previous 59,000 175,000 159,000 70,000 70,000 150,000 90,000 Change ▲5.6% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼5.9% Njombe Current 52,500 175,000 175,000 NA NA 160,000 56,600 Previous 50,000 175,000 175,000 NA NA 160,000 45,300 Change ▲4.8% ►0.0% ►0.0% ►0.0% ▲20.0% Kilimanjaro Current 60,000 180,000 185,000 120,000 120,000 NA 70,000 Previous 60,000 180,000 185,000 120,000 120,000 NA 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Katavi Current 64,000 150,000 220,000 190,000 NA 190,000 63,100 Previous 48,000 145,000 220,000 190,000 NA 190,000 57,500 Change ▲25.0% ▲3.3% ►0.0% ►0.0% ►0.0% ▲8.9% Kigoma Current 62,500 159,000 135,000 90,000 100,000 150,000 81,300 Previous 61,500 159,000 110,000 100,000 100,000 180,000 65,000 Change ▲1.6% ►0.0% ▲18.5% ▼11.1% ►0.0% ▼20.0% ▲20.0% 4 Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,820.88 1950.95 487.74 520.25 Retail 1,950.95 2,276.11 845.41 910.44 Source: https://farmgainafrica.org/ Date 31st December, 2021 Table 4: Average Retail prices (TZS) for Horticulture products for a week ending 9 - 15 December 2021 Tomato Onion (Local) Watermelon Pineapple Green Pepper Cucumber Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kg) Pineapple (Kg) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 46,200 176,000 616 550 33,000 88,000 Mombasa 48,400 154,000 660 44,000 88,000 Zanzibar 40,000 200,000 700 600 32,500 65,000 Dar es salaam 42,500 220,000 325 250 50,000 120,000 Morogoro 55,000 170,000 475 1,000 41,667 70,833 Dodoma 40,000 180,000 300 275 33,333 60,000 Shinyanga 35,000 208,333 800 500 29,167 40,000 Mwanza 30,000 225,000 500 800 18,000 30,000 Arusha 41,000 200,000 515 500 27,500 55,000 Tanga 43,333 170,000 567 533 35,000 66,667 Lindi 45,000 172,917 700 550 37,500 52,500 Mtwara 42,500 166,667 500 400 75,000 90,000 Mbeya 50,000 105,000 615 840 53,000 107,000 Average 42,995 180,609 559 567 39,205 71,769 Source: TAHA, 2021 5 Table 5: Coffee sales (by varieties) for 2021/22 trade season ending 13th December, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 15,269,311 57,577,564 10,804,230 45,976,523 31,536 114,494.50 26,105,077 103,668,581.07 Hard Arabika - - 192,865 493,286 - - 192,865 493,285.82 Robusta 848,920 1,869,678 23,151,562 36,726,395 285,176 408,441.68 24,285,658 39,004,514.60 Total 16,118,231 59,447,242 34,148,657 83,196,204 316,712 522,936.18 50,583,600 143,166,381.49 Source: Tanzania Coffee Board, 2021 Table 6: Cocoa sales for 2021/22 trade season ending 29th December, 2021 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 4,616,650 1,278,240 472,940 6,367,830.00 4,981.79 30,135,229,660.00 Source: Tanzania Cooperative Development Commission, 2021 Table 7: Cashew nut sales for 2021/22 trade season ending 24th December, 2021. Union Name Amount sold (Kg) Maximum price (TZS/Kg) SG Minimum price (TZS/Kg) SG Maximum price (TZS/Kg ) UG Minimum price (TZS/Kg) SG Value of amount sold (TZS) TANECU 54,795,617 2,445 1,670 1,705 1,580 118,684,105,154 MAMCU 67,949,015 2,400 1,805 1,715 1,200 145,918,006,339 LINDI MWAMBAO 19,415,656 2,286 1,700 1,710 1,635 40,156,622,949 RUNALI 43,955,044 2,352 1,815 98,055,721,388 CORECU 13,065,121 2,060 1,900 1,755 1,320 24,747,831,958 TAMCU 21,096,238 2,267 1,811 43,224,648,301 JUMLA YA MAUZO YOTE 220,276,691 2,445 1,700 1,755 1,580 470,786,936,089 Source: Tanzania Cashewnut Board, 2021 NB: SG: Standard Grade UG: Under Grade 6 Table 8: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 Figure 1: Average price trend of DAP in the world market as season of 23rd December, 2021 Source: TFRA, 2021 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0 24 Jun 2021 01 Jul 2021 08 Jul 2021 15 Jul 2021 22 Jul 2021 29 Jul 2021 05 Aug 2021 12 Aug 2021 19 Aug 2021 26 Aug 2021 02 Sep 2021 09 Sep 2021 16 Sep 2021 23 Sep 2021 30 Sep 2021 07 Oct 2021 14 Oct 2021 21 Oct 2021 28 Oct 2021 04 Nov 2021 11 Nov 2021 18 Nov 2021 25 Nov 2021 02 Dec 2021 09 Dec 2021 16 Dec 2021 23 Dec 2021 Price (USD/Tone) Period 7 Figure 2: Average price trend of Urea in the world market as season of 23th December, 2021 Source: TFRA, 2021 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace Contacts Ag: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122 0.0 100.0 200.0 300.0 400.0 500.0 600.0 700.0 800.0 900.0 1000.0 06 May 2021 13 May 2021 20 May 2021 27 May 2021 03 Jun 2021 10 Jun 2021 17 Jun 2021 24 Jun 2021 01 Jul 2021 08 Jul 2021 15 Jul 2021 22 Jul 2021 29 Jul 2021 05 Aug 2021 12 Aug 2021 19 Aug 2021 26 Aug 2021 02 Sep 2021 09 Sep 2021 16 Sep 2021 23 Sep 2021 30 Sep 2021 07 Oct 2021 14 Oct 2021 21 Oct 2021 28 Oct 2021 04 Nov 2021 11 Nov 2021 18 Nov 2021 25 Nov 2021 02 Dec 2021 09 Dec 2021 16 Dec 2021 23 Dec 2021 Price USD /Tone Period 8 Annex: Rainfall Outlook Nov, 2021- April, 2022 Source: Tanzania Meteorological Authority

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin 28 Mar – 1 Apr, 2022 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Mar 21 - 25, 2022 Current week Mar 28 - Apr 1, 2022 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 62,500 185,600 186,800 120,400 118,800 167,100 79,400 Previous 60,600 188,300 185,500 120,400 113,400 165,100 76,700 Change ▲3.0% ▼1.5% ▲0.7% ►0.0% ▲4.5% ▲1.2% ▲3.4% Key Messages This week, wholesale prices for food crops have increased and decreased at different rates compared to price levels last week. Some variations in the average prices of crops were observed across the markets. Prices for bulrush millet, round potatoes, maize, finger millet and beans increased by an average 4.5%, 3.4%, 3.0%, 1.2% and 0.7% respectively. Price for rice decreased by 1.5%, price of sorghum remained constant. Coffee: Total sales were 63,904,458 kilograms with a total value of USD 196 million for the week ended 30th March, 2022. Cocoa: Total sales were 7,502,070 kilograms with a total value of TZS 35 billion for the week ended 28th March, 2022. Cashew nut: Until 27th March, 2022, trade season total sale of raw cashew nut was 231,155,915 kilograms with a total value of TZS 489 billion. Fertilizer: Until 31st March, 2022 average price in the world market of DAP has increased by 3%, while averages prices of UREA in world market has decreased by 0.5%. compared to last week average price. The ongoing Russia-Ukraine conflict, as well as the Covid-19 supply chain disruptions, have all contributed to rising fertilizer prices. A weather report released by the meteorological authority shows rainfall (November, 2021-April, 2022) is anticipated to be below average in several areas (Annex 1). This might result in the rise of price of food crops. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 53,300 187,000 187,500 63,300 66,000 137,300 93,100 Previous 51,800 187,000 187,000 62,900 66,000 138,500 81,800 Change ▲2.8% ►0.0% ▲0.3% ▲0.6% ►0.0% ▼0.9% ▲12.1% Arusha Current 61,000 210,000 175,000 62,500 69,000 138,500 95,000 Previous 59,500 210,000 175,000 62,500 67,500 138,500 82,500 Change ▲2.5% ►0.0% ►0.0% ►0.0% ▲2.2% ►0.0% ▲13.2% Dar es Salaam Current 79,000 210,000 230,000 110,000 85,000 170,000 61,300 Previous 67,000 205,000 235,000 110,000 80,000 170,000 61,900 Change ▲15.2% ▲2.4% ▼2.2% ►0.0% ▲5.9% ►0.0% ▼1.0% Lindi Current 75,000 205,000 195,000 175,000 NA 180,000 92,500 Previous 75,000 205,000 195,000 175,000 NA 180,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Morogoro Current 58,800 198,500 195,000 175,000 175,000 172,500 92,500 Previous 58,800 198,500 195,000 175,000 175,000 172,500 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tanga Current 77,500 190,000 195,000 100,000 110,000 170,000 87,800 Previous 65,000 190,000 195,000 100,000 110,000 170,000 85,600 Change ▲16.1% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲2.5% Mtwara Current 55,000 190,000 205,000 NA NA 180,000 NA Previous 55,000 190,000 205,000 NA NA 180,000 NA Change ►0.0% ►0.0% ►0.0% ►0.0% Iringa Current 49,000 210,000 205,000 110,000 NA 150,000 56,300 Previous 47,000 205,000 205,000 110,000 NA 150,000 56,300 Change ▲4.1% ▲2.4% ►0.0% ►0.0% ►0.0% ►0.0% Rukwa Current 55,000 165,000 170,000 NA NA NA 55,000 Previous 54,300 167,500 173,800 NA NA NA 62,500 Change ▲1.3% ▼1.5% ▼2.2% ▼13.6% Kigoma Current 61,000 170,000 110,000 90,000 125,000 190,000 100,000 Previous 61,000 170,000 110,000 90,000 125,000 190,000 100,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Shinyanga Current 57,500 170,000 175,000 95,000 95,000 NA 67,500 Previous NA NA NA NA NA NA NA Change 3 Source: Ministry of Investment, Industry and Trade Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change in price. ✓ N/A - data not available. Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Mwanza Current 69,000 177,500 192,500 150,000 170,000 180,000 92,500 Previous 69,000 177,500 192,500 150,000 170,000 180,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 56,500 167,500 150,000 130,000 130,000 140,000 62,500 Previous 56,500 167,500 150,000 130,000 130,000 140,000 62,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Mara Current 65,000 150,000 227,500 62,500 190,000 190,000 92,500 Previous NA NA NA NA NA NA NA Change Manyara Current 63,300 190,000 159,000 90,000 90,000 155,000 105,000 Previous 60,000 190,000 159,000 90,000 90,000 150,000 85,000 Change ▲5.2% ►0.0% ►0.0% ►0.0% ►0.0% ▲3.2% ▲19.0% Njombe Current 58,000 220,000 185,000 NA NA 162,500 50,500 Previous 58,000 220,000 185,000 NA NA 162,500 50,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kilimanjaro Current 67,500 180,000 185,000 120,000 120,000 NA 70,000 Previous 67,500 180,000 185,000 120,000 120,000 NA 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Katavi Current 64,000 150,000 220,000 190,000 NA 190,000 75,000 Previous 64,000 150,000 220,000 190,000 NA 190,000 75,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 4 Table 3: Average Retail prices (TZS) for Horticulture products for a week of 25th – 30th March, 2022 Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kg) Pineapple (Kg) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 44,000 206,800 748 660 90,933 46,200 Mombasa 45,467 261,250 660 62,333 117,333 Dar es salaam 43,250 322,500 400 400 60,417 152,500 Morogoro 36,000 293,333 500 733 41,667 67,778 Dodoma 40,000 300,000 367 600 25,833 57,778 Shinyanga 30,000 179,167 625 500 27,083 55,000 Mwanza 36,000 233,333 533 917 26,667 50,000 Arusha 48,333 247,222 733 567 36,667 51,667 Tanga 35,000 188,750 525 675 32,500 77,500 Lindi 48,333 247,222 733 567 36,667 51,667 Mtwara 86,667 250,000 400 533 83,333 75,000 Mbeya 44,000 242,917 1,250 1,313 46,000 120,500 Average 44,754 247,708 623 679 47,508 76,910 Source: TAHA, 2022 Table 4: Coffee sales (by varieties) for 2021/22 trade season ending 30th March, 2022 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabica 17,443,712 66,463,399 18,813,020 83,588,763 73,283 244,327.36 36,330,015 150,296,489 Hard Arabica 15,760 38,814 665,385 1,446,056 5,400 4,320.00 686,545 1,489,190 Robusta 1,243,294 2,732,643 25,314,488 411,110,040 330,116 475,323.49 26,887,898 44,318,007 Total 18,702,766 69,234,856 44,792,893 126,144,856 408,799 723,970.85 63,904,458 196,108,686 Source: Tanzania Coffee Board, 2022 5 Table 5: Cocoa sales for 2021/22 trade season ending 28th March, 2022 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 5,614,040 1,385,060 502,970 7,502,070 4,763.58 35,225,557,130 Source: Tanzania Cooperative Development Commission, 2022 Table 6: Cashew nut sales for 2021/22 trade season ending 27th March, 2022 Union Name Amount sold (Kg) Maximum price (TZS/Kg) SG Minimum price (TZS/Kg) SG Maximum price (TZS/Kg) UG Minimum price (TZS/Kg) SG Value of amount sold (TZS) TANECU 56,897,028 2,445 1,615 1,705 1,305 122,053,954,954 MAMCU 69,065,290 2,400 1,350 1,715 1,200 147,755,070,189 LINDI MWAMBAO 19,415,656 2,286 1,700 1,710 1,635 40,156,622,949 RUNALI 46,372,299 2,352 1,400 102,113,318,103 CORECU 13,272,882 2,060 1,900 1,755 1,320 25,015,289,408 TAMCU 25,284,493 2,267 1,700 50,534,029,863 MOFACU 137,080 2,270 1,850 292,763,930 TACACU 711,187 1,700 1,610 1,250 1,200 1,137,802,010 TOTAL SALES 231,155,915 2,445 1,500 1,755 1,200 489,058,851,406 Source: Tanzania Cashewnut Board, 2022 NB: SG: Standard Grade UG: Under Grade Table 7: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 6 Figure 1: Average price trend of UREA fertilizer in the world market as a season of 31st March, 2022 Source: TFRA, 2022 0.0 200.0 400.0 600.0 800.0 1,000.0 1,200.0 1,400.0 06 May 2021 13 May 2021 20 May 2021 27 May 2021 03 Jun 2021 10 Jun 2021 17 Jun 2021 24 Jun 2021 01 Jul 2021 08 Jul 2021 15 Jul 2021 22 Jul 2021 29 Jul 2021 05 Aug 2021 12 Aug 2021 19 Aug 2021 26 Aug 2021 02 Sep 2021 09 Sep 2021 16 Sep 2021 23 Sep 2021 30 Sep 2021 07 Oct 2021 14 Oct 2021 21 Oct 2021 28 Oct 2021 04 Nov 2021 11 Nov 2021 18 Nov 2021 25 Nov 2021 02 Dec 2021 09 Dec 2021 16 Dec 2021 23 Dec 2021 30 Dec 2021 06 Jan 2022 13 Jan 2022 20 Jan 2022 27 Jan 2022 03 Feb 2022 10 Feb 2022 17 Feb 2022 24 Feb 2022 03 Mar 2022 10 Mar 2022 17 Mar 2022 24 Mar 2022 31 Mar 2022 Price (USD/Ton) Period 7 Figure 2: Average price trend of DAP fertilizer in the world market as a season of 31st March, 2022 Source: TFRA, 2022 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders through their mobile phones, how to access services; - ❖ USSD: Dial *152*00# select No. 7 then Na. 2 then follow the instructions ❖ Website: open exts.kilimo.go.tz then select the service Contacts Agricultural Marketing Section, Ministry of Agriculture, P.O. Box 2182, DODOMA. Email: [email protected] 8 Annex: Rainfall Outlook Nov, 2021- April, 2022 Source: Tanzania Meteorological Authority

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin 23 – 27 May, 2022 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Key Messages Food crops: Wholesale prices for food crops have increased and decreased at different rates compared to price levels last week, prices of bulrush millet, finger millet and round potatoes have increased by an average 10.7%, 5.9% and 2.9% respectively. Prices for sorghum, rice, beans and maize, decreased by an average of 7.2%, 4.3%, 2.1% and 0.1% respectively. Horticulture: A week of 12tH – 18th May, 2022, prices of different markets in the country for horticultural crops changed at different rates. Price of cucumber increased by 4% while the prices of green pepper, pineapple, tomato and onion decreased by 29%, 25%, 11%, and 6% respectively. Price of water melon remains constant. Cocoa: Total sales were 8,673,030 kilograms with a total value of TZS 40 billion for the week ended 18th May, 2022. Cashew nut: Until 05th April, 2022, trade season total sale of raw cashew nut was 231,199,729 kilograms with a total value of TZS 489 billion. Fertilizer: Until 26th May, 2022 average price in the world market of UREA decreased by 4% and DAP increased by 5% compared to last week average price. Previous week May 16 - 20, 2022 Current week May 23 - 27, 2022 Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 70,900 193,000 183,900 120,100 125,800 177,600 72,900 Previous 71,000 201,300 187,700 128,800 112,400 167,200 70,800 Change ▼0.1% ▼4.3% ▼2.1% ▼7.2% ▲10.7% ▲5.9% ▲2.9% National Average 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 73,400 205,000 200,500 66,300 72,500 147,500 69,500 Previous 74,800 203,300 197,000 65,200 66,500 149,000 76,800 Change ▼1.9% ▲0.8% ▲1.7% ▲1.7% ▲8.3% ▼1.0% ▼10.5% Arusha Current 81,500 220,000 170,000 67,500 NA 139,500 83,500 Previous 78,500 220,000 170,000 67,500 NA 139,500 87,500 Change ▲3.7% ►0.0% ►0.0% ►0.0% ►0.0% ▼4.8% Dar es Salaam Current 95,000 192,500 205,000 145,000 100,000 167,500 59,000 Previous 89,800 217,500 222,500 130,000 97,500 175,000 60,800 Change ▲5.5% ▼13.0% ▼8.5% ▲10.3% ▲2.5% ▼4.5% ▼3.1% Lindi Current 100,000 225,000 230,000 145,000 NA 225,000 80,000 Previous 90,000 225,000 225,000 145,000 NA 235,000 80,000 Change ▲10.0% ►0.0% ▲2.2% ►0.0% ▼4.4% ►0.0% Morogoro Current 71,300 215,000 185,000 175,000 175,000 177,500 97,500 Previous 62,500 215,000 185,000 175,000 175,000 177,500 82,500 Change ▲12.3% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲15.4% Tanga Current 79,300 191,300 195,000 100,000 100,000 187,500 65,000 Previous 79,300 190,000 195,000 100,000 100,000 183,000 65,000 Change ►0.0% ▲0.7% ►0.0% ►0.0% ►0.0% ▲2.4% ►0.0% Mtwara Current 65,000 195,000 177,500 NA NA 180,000 NA Previous 65,000 210,000 177,500 NA NA 180,000 NA Change ►0.0% ▼7.7% ►0.0% ►0.0% Iringa Current 58,800 180,000 180,000 150,000 NA 180,000 50,000 Previous 61,800 195,000 180,000 150,000 NA 180,000 55,000 Change ▼5.1% ▼8.3% ►0.0% ►0.0% ►0.0% ▼10.0% Ruvuma Current 49,000 185,000 165,000 NA NA NA 77,500 Previous 50,000 190,000 170,000 NA NA NA 77,500 Change ▼2.0% ▼2.7% ▼3.0% ►0.0% 3 Source: Ministry of Investment, Industry and Trade Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change in price. ✓ N/A - data not available. Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Tabora Current 60,500 192,500 202,500 NA NA NA NA Previous 60,500 175,000 190,000 NA NA NA NA Change ►0.0% ▲9.1% ▲6.2% Rukwa Current 61,300 167,500 157,500 NA NA NA 66,300 Previous 65,000 150,000 162,500 NA NA NA 67,500 Change ▼6.0% ▲10.4% ▼3.2% ▼1.8% Kigoma Current 65,300 175,000 155,000 100,000 100,000 185,000 87,500 Previous NA NA NA NA NA NA NA Change Shinyanga Current 62,500 170,000 175,000 105,000 95,000 125,000 85,000 Previous 66,300 196,300 175,300 92,500 87,500 115,000 78,800 Change ▼6.1% ▼15.5% ▼0.2% ▲11.9% ▲7.9% ▲8.0% ▲7.3% Kagera Current 81,000 190,000 170,000 135,000 165,000 160,000 70,000 Previous NA NA NA NA NA NA NA Change Mara Current 72,500 170,000 210,000 72,500 235,000 235,000 100,000 Previous NA NA NA NA NA NA NA Change Manyara Current 74,500 205,000 165,000 110,000 90,000 155,000 70,000 Previous 74,500 205,000 165,000 110,000 90,000 155,000 72,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼3.6% Njombe Current 66,000 225,000 185,000 NA NA 210,000 48,300 Previous NA NA NA NA NA NA NA Change Katavi Current 59,500 170,000 182,500 190,000 NA 190,000 57,500 Previous 51,000 170,000 180,000 190,000 NA 190,000 57,500 Change ▲14.3% ►0.0% ▲1.4% ►0.0% ►0.0% ►0.0% 4 Table 3: Average Retail prices (TZS) for Horticulture products for a week of 12tH – 18th May, 2022 Region Tomato (40 Kg Crate) Onion (100 Kg Sack) Watermelon (Kg) Pineapple (Kg) Green Pepper (50 Kg Sack) Cucumber (100 Kg Sack) Nairobi 57,200 211,200 741 689 72,600 69,667 Mombasa 53,350 176,458 660 60,500 71,500 Zanziber 43,750 277,500 775 825 115,000 70,000 Dar es Salaam 43,000 242,500 440 400 70,833 120,000 Morogoro 270,000 500 800 41,667 Dodoma 20,000 287,500 238 825 36,667 63,750 Shinyanga 40,000 166,667 500 625 25,000 60,000 Mwanza 32,500 233,333 675 1,125 25,000 37,500 Arusha 45,000 214,583 650 750 37,500 82,500 Tanga 46,250 245,000 675 1,000 41,250 75,000 Lindi 45,000 214,583 650 750 37,500 82,500 Mtwara 48,333 210,000 500 700 100,000 100,000 Mbeya 37,000 128,889 597 1,383 49,333 122,667 Current average 42,615 221,401 585 823 54,835 79,590 Previous average 47,849 234,799 584 1,096 77,004 76,500 Change ▼11% ▼ 6% 0 ► ▼25% ▼29% ▲4% Source: TAHA, 2022 Table 5: Cocoa sales for 2021/22 trade season ending 18th May, 2022 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 6,624,490 1,551,890 518,790 8,673,030 4,744.71 40,432,882,120 Source: Tanzania Cooperative Development Commission, 2022 5 Table 6: Cashew nut sales for 2021/22 trade season ending 05th April, 2022 Union Name Amount sold (Kg) Maximum price (TZS/Kg) SG Minimum price (TZS/Kg) SG Maximum price (TZS/Kg) UG Minimum price (TZS/Kg) SG Value of amount sold (TZS) TANECU 56,897,028 2,445 1,615 1,705 1,305 122,053,954,954 MAMCU 69,065,290 2,400 1,350 1,715 1,200 147,755,070,189 LINDI MWAMBAO 19,415,656 2,286 1,700 1,710 1,635 40,156,622,949 RUNALI 46,372,299 2,352 1,400 102,113,318,103 CORECU 13,272,882 2,060 1,900 1,755 1,320 25,015,289,408 TAMCU 25,284,493 2,267 1,700 50,534,029,863 MOFACU 137,080 2,270 1,850 292,763,930 TACACU 731,287 1,700 1,610 1,250 1,200 1,161,922,010 CEAMCU 23,714 1,610 38,179,540.00 TOTAL SALES 231,199,729 2,445 1,500 1,755 1,200 489,121,150,946 Source: Tanzania Cashewnut Board, 2022 NB: SG: Standard Grade UG: Under Grade Table 7: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2022 6 Figure 1: Average price trend of UREA fertilizer in the world market as a season of 26th May, 2022 Source: TFRA, 2022 Figure 2: Average price trend of DAP fertilizer in the world market as a season of 26th May, 2022 Source: TFRA, 2022 0 100 200 300 400 500 600 700 800 900 100022 Jul 202105 Aug 202119 Aug 202102 Sep 202116 Sep 202130 Sep 202114 Oct 202128 Oct 202111 Nov 202125 Nov 202109 Dec 202123 Dec 202106 Jan 202220 Jan 202203 Feb 202217 Feb 202203 Mar 202217 Mar 202231 Mar 202214 Apr 202228 Apr 202212 May 202226 May 2022 Price (USD/T) Period 0 200 400 600 800 1000 1200 140022 Jul 202105 Aug 202119 Aug 202102 Sep 202116 Sep 202130 Sep 202114 Oct 202128 Oct 202111 Nov 202125 Nov 202109 Dec 202123 Dec 202106 Jan 202220 Jan 202203 Feb 202217 Feb 202203 Mar 202217 Mar 202231 Mar 202214 Apr 202228 Apr 202212 May 202226 May 2022 Price (USD/T) Period 7 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders through their mobile phones, how to access services; - ❖ USSD: Dial *152*00# select No. 7 then Na. 2 then follow the instructions ❖ Website: open exts.kilimo.go.tz then select the service Contacts Agricultural Marketing Section, Ministry of Agriculture, P.O. Box 2182, DODOMA. Email: [email protected]

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin October 04-08, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Sept 27- Oct 01 Current week Oct 04-08 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 45,900 146,700 162,500 92,800 105,700 154,100 61,300 Previous 45,800 145,400 162,800 93,900 112,200 145,400 58,800 Change ▲0.2% ▲0.9% ▼0.2% ▼1.2% ▼6.1% ▲5.6% ▲4.1% Key Messages Overall, the National average wholesale prices of major food crops increased compared to their levels a week earlier (table 1). Some variations in the prices of crops were observed across the markets (Table2). Prices for maize, rice, finger millet, and round potato increased by 0.2%, 0.9%, 5.6%, and 4.1%, respectively. On the other hand, price of beans, sorghum, and bulrush millet declined by 0.2%, 1.2% and 6.1% respectively Cotton: Total sales were 143,838,683 kilograms for the week ended 29th September. Coffee: Total sales were 27,996,806 kilograms with a total value of USD 61.6 mil for the week ended 01st October, 2021 Pigeon peas: Total sales were 3,169,976 kilograms with a total value of TZS 4.2 billion for the week ended 22nd September, 2021. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 42,800 135,000 182,500 48,300 47,000 123,500 53,000 Previous 43,500 140,000 185,000 50,000 54,500 121,500 53,300 Change ▼1.6% ▼3.7% ▼1.4% ▼3.5% ▼16.0% ▲1.6% ▼0.6% Arusha Current 48,800 162,500 160,000 57,500 61,000 135,500 42,500 Previous 47,500 162,500 160,000 63,500 72,500 132,500 42,500 Change ▲2.7% ►0.0% ►0.0% ▼10.4% ▼18.9% ▲2.2% ►0.0% Dar es Salaam Current 51,700 158,300 203,300 85,000 80,000 152,500 56,000 Previous 51,800 166,700 205,000 87,500 82,500 162,500 55,200 Change ▼0.2% ▼5.3% ▼0.8% ▼2.9% ▼3.1% ▼6.6% ▲1.4% Tanga Current 50,500 120,000 150,000 85,000 100,000 170,000 44,000 Previous 50,000 120,000 150,000 85,000 100,000 170,000 43,000 Change ▲1.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲2.3% Ruvuma Current 28,500 175,000 142,500 NA NA NA 73,500 Previous 27,500 170,000 140,000 NA NA NA 73,500 Change ▲3.5% ▲2.9% ▲1.8% ►0.0% Iringa Current 32,000 160,000 145,000 90,000 NA 150,000 45,000 Previous 32,000 160,000 145,000 90,000 NA 150,000 45,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Njombe Current 40,000 215,000 175,000 NA NA 160,000 42,800 Previous wee 40,000 215,000 175,000 NA NA 160,000 42,800 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Rukwa Current 31,000 110,000 137,500 NA NA 125,000 52,500 Previous 31,000 112,500 147,500 NA NA 125,000 52,500 Change ►0.0% ▼2.3% ▼7.3% ►0.0% ►0.0% 3 Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,808 1,937 484 516 527 Retail 1,937 2260 839 904 922 Source: https://farmgainafrica.org/ Date 01st Oktober, 2021 Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Kagera Current 57,000 127,500 135,000 90,000 70,000 145,000 67,500 Previous 58,000 127,000 130,000 87,500 105,000 155,000 57,500 Change ▼1.8% ▲0.4% ▲3.7% ▲2.8% ▼50.0% ▼6.9% ▲14.8% Mara Current 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Previous 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 46,800 150,000 135,000 74,000 90,000 125,000 52,000 Previous 46,000 150,000 135,000 74,000 90,000 125,000 52,000 Change ▲1.7% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Shinyanga Current 45,500 125,000 175,000 125,000 125,000 135,000 85,000 Previous 45,500 125,000 175,000 125,000 125,000 135,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Mtwara Current 42,500 165,000 167,500 60,000 NA 180,000 60,000 Previous 40,000 165,000 162,500 60,000 NA 180,000 57,500 Change ▲5.9% ►0.0% ▲3.0% ►0.0% ►0.0% ▲4.2% Kilimanjaro Current 55,000 175,000 150,000 120,000 140,000 NA 75,000 Previous 52,500 175,000 150,000 120,000 140,000 NA 70,000 Change ▲4.5% ►0.0% ►0.0% ►0.0% ►0.0% ▲6.7% Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Tabora Current 39,000 102,500 170,000 145,000 NA 175,000 52,500 Previous 39,000 102,500 170,000 145,000 NA 175,000 52,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Mwanza Current 64,000 180,000 205,000 175,000 190,000 200,000 80,000 Previous 64,000 180,000 205,000 175,000 190,000 200,000 80,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kigoma Current 43,000 127,500 135,000 90,000 70,000 145,000 67,500 Previous 47,200 95,000 137,500 90,000 85,000 145,000 45,000 Change ▼9.8% ▲25.5% ▼1.9% ►0.0% ▼21.4% ►0.0% ▲33.3% 4 Table 3: Prices for rice and maize in Uganda (TZS/Kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,808 1,937 484 516 Retail 1,937 2260 839 904 Source: https://farmgainafrica.org/ Date 8th October, 2021 Table 4: Cotton sales for 2021/22 trade season week no.20 ending 20th September, 2021 Sales Transportation Previous weeks (KGS) Current week (KGS) Total (KGS) Previous weeks (KGS) Current week (KGS) Total (KGS) 143,196,869 532,605 143,729,474 143,305,637 533,046 4143,838,683 Source: Tanzania Cotton Board, 2021 Table 5: Pigeon peas sales for 2021/22 season as of 22nd September, 2021 Date District AMCOS Company Amount (Kg) TZS/Kg TZS 11/08/2021 Namtumbo Ushirika B Afrisian Ginning 41,428 1,280 53,027,840 12/08/2021 Tunduru Mtetesi MeTL 150,000 1,300 195,000,000 Tunduru Mtetesi RBST 210,251 1,290 271,223,790 18/08/2021 Namtumbo Ushirika B MeTL 206,517 1,320 272,602,440 Namtumbo Ushirika B LENIC 200,000 1,320 264,000,000 19/08/2021 Tunduru Asema LENIC 300,000 1,344 403,200,000 Tunduru Asema LENIC 845,498 1,340 1,132,967,320 25/08/2021 Namtumbo Namtumbo LENIC 819,149 1,380 1,083,734,127 16/09/2021 Tunduru Mruji LENIC 325,184 1,110 360,954,240 22/09/2021 Namtumbo Ushirika B LENIC 71,949 990 71,229,510 GRAND TOTAL 3,169,976 4,154,630,760 5 Table 6: Coffee sales (by varieties) for 2021/22 trade season ending 01st October, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 7,036,956 25,066,185 1,568,600 6,052,981 8,605,556 31,308,712 Hard Arabika 169,860 425,727 169,860 425,727.12 Robusta 331,770 686,812 18,622,444 28,753,925 285,176 408,441.68 19,221,390 29,819,178.92 Total 7,350,726 25,752,998 20,360,904 35,422,178 285,176 408,441.68 27,996,806 61,583,617.33 Source: Tanzania Coffee Board, 2021 Table 7: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace ✓ Weather Forecast October-December 2021. o Vuli rains are expected to be below normal to normal and characterized by prolonged periods of dry spells. o The Vuli rainy season is expected to have a poor start in the third and fourth weeks of October 2021 with poor distribution in many areas. o Besides the below normal to normal rainfall condition, warmer than usual temperatures are expected across bimodal areas during the Vuli rainy season. 6 o For more information visit |Tanzania Meteorological Authority Contacts Ag: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin October 18-22, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Oct 11-15 Current week Oct 18-22 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 43,500 141,700 158,100 89,600 111,600 148,700 59,900 Previous 46,200 149,600 165,600 98,700 111,500 157,700 61,600 Change ▼6.2% ▼5.6% ▼4.7% ▼10.2% ▲0.1% ▼6.1% ▼2.8% Key Messages Overall, the National average wholesale prices of major food crops decreased compared to their levels a week earlier (table 1). Some variations in the prices of crops were observed across the markets (Table2). Prices for sorghum, maize, finger millet, rice, beans, and round potatoes, decreased by 10.2%, 6.2% 6.1 %, 5.6%, 4.7%, and 2.8% respectively, while the prices bulrush millet increased significantly by 0.1%. Cotton: Total sales were 144,011,169 kilograms for the week ended 26th September 2021. Coffee: Total sales were 32,534,107 kilograms with a total value of USD 78.7 million for the week ended 15th October, 2021 Cocoa: Total sales were 3,966,570 kilograms with a total value of TZS 19.6 billion for the week ended 18th October, 2021. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 42,300 139,000 185,000 49,500 45,000 120,300 52,500 Previous 43,000 140,000 184,000 52,100 47,500 125,000 53,800 Change ▼1.7% ▼0.7% ▲0.5% ▼5.3% ▼5.6% ▼3.9% ▼2.5% Arusha Current 50,500 165,000 155,000 67,500 67,500 132,500 52,500 Previous 50,300 162,500 155,000 62,500 63,500 135,500 52,500 Change ▲0.4% ▲1.5% ►0.0% ▲7.4% ▲5.9% ▼2.3% ►0.0% Dar es Salaam Current 48,000 175,000 215,000 85,000 90,000 165,000 65,000 Previous 47,800 167,500 202,500 90,000 95,000 170,000 56,500 Change ▲0.4% ▲4.3% ▲5.8% ▼5.9% ▼5.6% ▼3.0% ▲13.1% Morogoro Current 47,300 175,000 180,000 120,000 120,000 180,000 79,000 Previous 46,900 175,000 180,000 120,000 120,000 177,500 79,000 Change ▲0.8% ►0.0% ►0.0% ►0.0% ►0.0% ▲1.4% ►0.0% Tanga Current 48,600 145,000 170,000 85,000 100,000 170,000 50,000 Previous 48,600 132,500 170,000 85,000 100,000 170,000 44,500 Change ►0.0% ▲8.6% ►0.0% ►0.0% ►0.0% ►0.0% ▲11.0% Mtwara Current 42,500 162,500 167,500 60,000 NA 180,000 52,500 Previous 42,500 165,000 167,500 60,000 NA 180,000 60,000 Change ►0.0% ▼1.5% ►0.0% ►0.0% ►0.0% ▼14.3% Iringa Current 33,500 160,000 160,000 90,000 NA 150,000 45,000 Previous 33,500 160,000 165,000 90,000 NA 150,000 45,000 Change ►0.0% ►0.0% ▼3.1% ►0.0% ►0.0% ►0.0% Ruvuma Current 29,000 170,000 140,000 NA NA NA 74,000 Previous 29,000 165,000 150,000 NA NA NA 70,000 Change ►0.0% ▲2.9% ▼7.1% ▲5.4% 3 Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Tabora Current 39,000 102,500 170,000 145,000 NA 175,000 52,500 Previous 39,000 102,500 170,000 145,000 NA 175,000 52,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Rukwa Current 31,500 110,000 150,000 NA NA 125,000 57,500 Previous 33,000 107,500 142,500 NA NA 122,500 53,800 Change ▼4.8% ▲2.3% ▲5.0% ▲2.0% ▲6.4% Kigoma Current 43,000 120,000 145,000 90,000 100,000 150,000 67,500 Previous 43,000 120,000 150,000 100,000 70,000 145,000 65,000 Change ►0.0% ►0.0% ▼3.4% ▼11.1% ▲30.0% ▲3.3% ▲3.7% Shinyanga Current 44,000 125,000 175,000 125,000 125,000 135,000 82,500 Previous 44,000 125,000 175,000 125,000 125,000 135,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼3.0% Mwanza Current 64,000 180,000 180,000 190,000 190,000 200,000 85,000 Previous 64,000 180,000 180,000 190,000 190,000 200,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 62,500 147,000 132,500 105,000 110,000 170,000 60,000 Previous 60,800 145,000 133,800 83,800 107,500 162,500 57,500 Change ▲2.7% ▲1.4% ▼1.0% ▲20.2% ▲2.3% ▲4.4% ▲4.2% Mara Current 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Previous 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 55,000 155,000 151,500 70,000 90,000 125,000 67,500 Previous 48,500 150,000 135,000 75,000 90,000 125,000 52,000 Change ▲11.8% ▲3.2% ▲10.9% ▼7.1% ►0.0% ►0.0% ▲23.0% Njombe Current 40,000 215,000 175,000 NA NA 160,000 42,800 Previous 40,000 215,000 175,000 NA NA 160,000 42,800 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 4 Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,800 1,950 480 510 Retail 1,950 2,250 850 900 Source: https://farmgainafrica.org/ Date 22nd October, 2021 Table 4: Cotton sales for 2021/22 trade season week no. 20 ending 26th September, 2021 Sales Transportation Previous weeks (Kg) Current week (Kg) Total (Kg) Previous weeks (Kg) Current week (Kg) Total (Kg) 142,726,153 1,285,016 144,011,169 142,822,682 612,315 143,434,997 Source: Tanzania Cotton Board, 2021 Table 5: Coffee sales (by varieties) for 2021/22 trade season ending 15th October, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 8,206,626 29,203,277 4,800,931 19,008,892 13,007,557 48,212,170 Hard Arabika - 169,860 425,727 169,860 425,727 Robusta 313,770 686,812 18,757,744 28,992,725 285,176 408,442 19,356,890 30,087,979 Total 8,520,392 29,890,090 23,728,535 48,427,344 285,176 408,442 32,534,107 78,725,876 Source: Tanzania Coffee Board, 2021 Table 6: Cocoa sales for 2021/22 season as of 18th October, 2021 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 2,813,750 857,960 294,860 3,966,570 4,896 19,567,018,610 5 Table 8: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - Market Place. ✓ Weather Forecast October-December 2021. o Vuli rains are expected to be below normal to normal and characterized by prolonged periods of dry spells. o The Vuli rainy season is expected to have a poor start in the third and fourth weeks of October 2021 with poor distribution in many areas. o Besides the below normal to normal rainfall condition, warmer than usual temperatures are expected across bimodal areas during the Vuli rainy season. o For more information visit |Tanzania Meteorological Authority] Contacts Ag Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected]

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section Weekly Market Bulletin October 25-29, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week Oct 18-22 Current week Oct 25-29 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 44,000 142,800 159,400 89,600 111,600 149,700 60,800 Previous 43,500 141,700 158,100 89,600 111,600 148,700 59,900 Change ▲1.1% ▲0.8% ▲0.8% ►0.0% ►0.0% ▲0.7% ▲1.5% Key Messages This week, wholesale prices for food crops have increased slightly compared to price levels last week (table 1). Some variations in the prices of crops were observed across the markets (Table2). Prices for round potatoes, maize, rice, beans and finger millet rose by an average of 1.5%, 1.1%, 0.8%, 0.8%, and 0.7% respectively. There were no changes in bullrush millet and sorghum prices. Cotton: Total sales were 144,011,169 kilograms for the week ended 26th September 2021. Coffee: Total sales were 34,367,654 kilograms with a total value of USD 84.9 million for the week ended 22nd October, 2021 Cocoa: Total sales were 4,271,210 kilograms with a total value of TZS 21 billion for the week ended 25th October, 2021. Cashew nut: Until 25th October, 2021, trade season total sale of raw cashew nuts was 41,401,086 kilograms with a total value of TZS 91.3 billion. A weather report released by the meteorological authority shows rainfall (November, 2021-April, 2022) is anticipated to be below average in several areas (Annex 1). This might result in the rise of price of food crops. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Morogoro Current 47,300 175,000 180,000 120,000 120,000 180,000 79,000 Previous 47,300 175,000 180,000 120,000 120,000 180,000 79,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tanga Current 49,800 147,500 172,500 85,000 100,000 170,000 50,000 Previous 48,600 145,000 170,000 85,000 100,000 170,000 50,000 Change ▲2.4% ▲1.7% ▲1.4% ►0.0% ►0.0% ►0.0% ►0.0% Mtwara Current 42,500 160,000 167,500 60,000 NA 180,000 52,500 Previous 42,500 162,500 167,500 60,000 NA 180,000 52,500 Change ►0.0% ▼1.6% ►0.0% ►0.0% ►0.0% ►0.0% Iringa Current 33,500 160,000 160,000 90,000 NA 150,000 45,000 Previous 33,500 160,000 160,000 90,000 NA 150,000 45,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Ruvuma Current 28,500 175,000 145,000 NA NA NA 74,000 Previous 29,000 170,000 140,000 NA NA NA 74,000 Change ▼1.8% ▲2.9% ▲3.4% ►0.0% Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 42,300 139,000 185,000 49,500 45,000 124,300 52,500 Previous 42,300 139,000 185,000 49,500 45,000 120,300 52,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲3.2% ►0.0% Arusha Current 49,500 170,000 155,000 67,500 67,500 132,500 52,500 Previous 50,500 165,000 155,000 67,500 67,500 132,500 52,500 Change ▼2.0% ▲2.9% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Dar es Salaam Current 48,000 175,000 215,000 85,000 90,000 165,000 65,000 Previous 48,000 175,000 215,000 85,000 90,000 165,000 65,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 3 Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Tabora Current 41,500 102,500 170,000 145,000 NA 175,000 58,800 Previous 39,000 102,500 170,000 145,000 NA 175,000 52,500 Change ▲6.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲10.7% Rukwa Current 32,800 112,500 161,300 NA NA 138,800 66,300 Previous 31,500 110,000 150,000 NA NA 125,000 57,500 Change ▲4.0% ▲2.2% ▲7.0% ▲9.9% ▲13.3% Kigoma Current 47,200 125,000 145,000 90,000 100,000 150,000 67,500 Previous 43,000 120,000 145,000 90,000 100,000 150,000 67,500 Change ▲8.9% ▲4.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Shinyanga Current 44,000 125,000 175,000 125,000 125,000 135,000 83,800 Previous 44,000 125,000 175,000 125,000 125,000 135,000 82,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲1.6% Mwanza Current 64,000 180,000 180,000 190,000 190,000 200,000 85,000 Previous 64,000 180,000 180,000 190,000 190,000 200,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 63,500 148,000 132,500 105,000 110,000 170,000 60,000 Previous 62,500 147,000 132,500 105,000 110,000 170,000 60,000 Change ▲1.6% ▲0.7% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Mara Current 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Previous 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 55,000 155,000 156,000 70,000 90,000 125,000 67,500 Previous 55,000 155,000 151,500 70,000 90,000 125,000 67,500 Change ►0.0% ►0.0% ▲2.9% ►0.0% ►0.0% ►0.0% ►0.0% Njombe Current 40,000 215,000 175,000 NA NA 160,000 42,800 Previous 40,000 215,000 175,000 NA NA 160,000 42,800 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% 4 Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,824 1,954 489 521 Retail 1,954 2,280 847 912 Source: https://farmgainafrica.org/ Date 29th October, 2021 Table 4: Cotton sales for 2021/22 trade season week no. 20 ending 26th September, 2021 Sales Transportation Previous weeks (Kg) Current week (Kg) Total (Kg) Previous weeks (Kg) Current week (Kg) Total (Kg) 142,726,153 1,285,016 144,011,169 142,822,682 612,315 143,434,997 Source: Tanzania Cotton Board, 2021 Table 5: Coffee sales (by varieties) for 2021/22 trade season ending 22nd October, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 9,505,023 34,258,160 4,800,931 19,008,892 14,305,954 53,267,052 Hard Arabika 12,640 32,624 169,860 425,727 182,500 458,351 Robusta 836,280 1,837,054 18,757,744 28,992,725 285,176 408,442 19,879,200 31,238,221 Total 10,353,943 36,127,838 23,728,535 48,427,344 285,176 408,442 34,367,654 84,963,624 Source: Tanzania Coffee Board, 2021 Table 6: Cocoa sales for 2021/22 season as of 25th October, 2021 LGA Amount sold (Kilo) Average price (TZS/Kg) Total (TZS) Kyela (Kg) Busokelo (Kg) Rungwe (Kg) 3,020,390 929,990 320,830 4,271,210 4,900.89 21,087,476,850 Source: Tanzania Cooperative Development Commission, 2021 5 Table 7: Cashew nut sales for 2021/22 trade season ending 24th October, 2021. Auction Date Union Name Amount Collected (Kgs) Amount Sold (Kgs) Value of amount Sold (Sh.) Maximum Price (TZS/ Kg) Minimum Price (TZS/ Kg) 10/8/2021 TANECU 1,486,267.00 1,486,267.00 3,420,523,352.00 2,445 2,231 10/15/2021 MAMCU 5,098,878.00 5,098,878.00 11,542,628,036.00 2,400 2,220 10/15/2021 TANECU 3,124,930.00 3,124,930.00 7,120,118,097.00 2,401 2,320 10/16/2021 LINDI MWAMBAO 5,012,932.00 5,012,932.00 10,978,586,607.00 2,286 2,100 10/17/2021 RUNALI 2,730,716.00 2,730,716.00 6,150,411,553.00 2,282 2,235 10/20/2021 CORECU 3,007,185.00 3,007,185.00 5,481,706,438.00 1,955 1,600 10/22/2021 MAMCU 7,400,607.00 7,400,607.00 16,470,184,510.00 2,321 2,070 10/22/2021 TANECU 5,990,924.00 5,990,924.00 13,577,069,466.00 2,311 2,255 10/23/2021 LINDI MWAMBAO 3,242,589.00 3,242,589.00 6,756,999,080.00 2,260 2,020 10/24/2021 RUNALI 4,306,058.00 4,306,058.00 9,776,043,560.00 2,311 2,240 JUMLA YA MAUZO YOTE 41,401,086.00 41,401,086.00 91,274,270,699.00 2,445 1,600 Source: Cashew nut Board Tanzania, 2021 Table 8: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 6 Figure 1: Average price trend of DAP in the world market Figure 2: Average price trend of Urea in the world market 0 100 200 300 400 500 600 700 800 04 Jun 2020 18 Jun 2020 02 Jul 2020 16 Jul 2020 30 Jul 2020 13 Aug 2020 27 Aug 2020 10 Sep 2020 24 Sep 2020 08 Oct 2020 22 Oct 2020 05 Nov 2020 19 Nov 2020 03 Dec 2020 17 Dec 2020 31 Dec 2020 14 Jan 2021 28 Jan 2021 11 Feb 2021 25 Feb 2021 11 Mar 2021 25 Mar 2021 08 Apr 2021 22 Apr 2021 06 May 2021 20 May 2021 03 Jun 2021 17 Jun 2021 01 Jul 2021 15 Jul 2021 29 Jul 2021 12 Aug 2021 26 Aug 2021 09 Sep 2021 23 Sep 2021 07 Oct 2021 21 Oct 2021 Price (USD/Tone) Period Average Price trend of DAP in the world market 0 100 200 300 400 500 600 700 800 04 Jun 2020 18 Jun 2020 02 Jul 2020 16 Jul 2020 30 Jul 2020 13 Aug 2020 27 Aug 2020 10 Sep 2020 24 Sep 2020 08 Oct 2020 22 Oct 2020 05 Nov 2020 19 Nov 2020 03 Dec 2020 17 Dec 2020 31 Dec 2020 14 Jan 2021 28 Jan 2021 11 Feb 2021 25 Feb 2021 11 Mar 2021 25 Mar 2021 08 Apr 2021 22 Apr 2021 06 May 2021 20 May 2021 03 Jun 2021 17 Jun 2021 01 Jul 2021 15 Jul 2021 29 Jul 2021 12 Aug 2021 26 Aug 2021 09 Sep 2021 23 Sep 2021 07 Oct 2021 21 Oct 2021 Prices (USD/Tone) Period Average Price Trend of Urea in the World Market 7 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace Contacts Ag: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122 8 Annex: Rainfall Outlook Nov, 2021- April, 2022 Source: Tanzania Meteorological Authority

# Extracted Content 1 Agricultural Marketing Section United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section WEEKLY MARKET BULLETIN September 06-10, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg) Previous week August 30- September 03 Current week September 06-10 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 41,700 134,000 162,100 86,600 106,600 154,000 59,700 Previous 41,900 133,100 163,400 87,100 102,600 145,900 59,800 Change ▼0.5% ▲0.7% ▼0.8% ▼0.6% ▲3.8% ▲5.3% ▼0.2% Key Messages Overall, prices of the main staple food commodities slightly decreased compared to their levels a week earlier with variations observed in spatial markets. Prices for maize, beans, sorghum, and round potatoes decreased by 0.5, 0.8, 0.6 and 0.2 percent respectively. Rice, bulrush millet, finger millet continued to increase by 0.7, 3.8, and 5.3 percent this week. Staple food prices are projected to trend seasonably due to the on-going May to August harvesting season which could result in increase in market supply and reduced market prices. Tobacco: Total sales were 55,722,247 kilograms with a total value of USD 86.4 mil for the week ended 22nd August. Cotton: Total sales were 138, 482,490 kilograms week ended 29th August. Pigeon peas: Total sales were 3,572,532 kilograms with a total value of TZS 4.68 billion for the week ended 02nd August. 2 Agricultural Marketing Section Table 2: Regional weekly average wholesale market prices (TZS/100 kg) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 38,700 142,500 185,000 50,200 49,500 122,500 62,000 Previous 39,300 142,500 182,500 48,500 48,500 120,000 63,000 Change ▼1.6% ►0.0% ▲1.4% ▲3.4% ▲2.0% ▲2.0% ▼1.6% Arusha Current 47,000 160,000 155,000 67,500 70,000 NA 42,500 Previous 47,500 160,000 155,000 69,000 71,000 NA 42,500 Change ▼1.1% ►0.0% ►0.0% ▼2.2% ▼1.4% ►0.0% Dar es Salaam Current 50,300 155,000 211,700 85,000 80,000 157,500 52,300 Previous 51,200 155,000 211,700 82,500 76,300 160,000 51,700 Change ▼1.8% ►0.0% ►0.0% ▲2.9% ▲4.6% ▼1.6% ▲1.1% Morogoro Current 39,500 170,000 180,000 120,000 120,000 160,000 70,000 Previous 39,500 150,000 182,500 120,000 115,000 160,000 74,000 Change ►0.0% ▲11.8% ▼1.4% ►0.0% ▲4.2% ►0.0% ▼5.7% Tanga Current 44,000 145,000 165,000 85,000 100,000 170,000 45,000 Previous 43,100 145,000 160,000 95,000 100,000 NA 45,500 Change ▲2.0% ►0.0% ▲3.0% ▼11.8% ►0.0% ▼1.1% Ruvuma Current 26,000 170,000 120,000 NA NA NA 73,500 Previous 27,000 175,000 130,000 NA NA NA 73,800 Change ▼3.8% ▼2.9% ▼8.3% ▼0.4% Iringa Current 34,000 160,000 145,000 90,000 NA 150,000 50,000 Previous 34,000 160,000 145,000 90,000 NA 150,000 50,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Njombe Current 36,500 145,000 152,000 NA NA 125,500 31,300 Previous wee 36,500 145,000 152,000 NA NA 125,500 31,300 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Rukwa Current 31,500 105,000 170,000 NA NA 175,000 65,000 Previous 34,300 105,000 173,800 NA NA 142,500 65,000 Change ▼8.9% ►0.0% ▼2.2% ▲18.6% ►0.0% 3 Agricultural Marketing Section Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Katavi Current 33,000 90,000 175,000 60,000 NA 165,000 45,000 Previous 31,300 90,000 175,000 60,000 NA 165,000 45,000 Change ▲5.2% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tabora Current 35,500 102,500 170,000 145,000 NA 175,000 62,500 Previous 35,500 102,500 170,000 145,000 NA 175,000 62,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kagera Current 55,000 130,000 130,000 125,000 155,000 155,000 60,000 Previous 54,500 135,000 130,000 125,000 127,500 150,000 60,000 Change ▲0.9% ▼3.8% ►0.0% ►0.0% ▲17.7% ▲3.2% ►0.0% Mara Current 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Previous 62,500 105,000 195,000 62,500 190,000 125,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲34.2% ►0.0% Manyara Current 45,000 150,000 135,000 70,000 90,000 125,000 65,000 Previous 45,000 150,000 135,000 70,000 90,000 125,000 62,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▲3.8% Shinyanga Current 43,500 110,000 175,000 115,000 105,000 115,000 85,000 Previous 43,500 110,000 177,500 115,000 105,000 115,000 85,000 Change ►0.0% ►0.0% ▼1.4% ►0.0% ►0.0% ►0.0% ►0.0% Mtwara Current 40,000 145,000 167,500 57,500 NA 180,000 57,500 Previous 40,000 145,000 167,500 57,500 NA 180,000 57,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Kigoma Current 47,200 93,500 125,000 80,000 NA 145,000 55,000 Previous 47,200 88,500 135,000 80,000 NA 150,000 55,000 Change ►0.0% ▲5.3% ▼8.0% ►0.0% ▼3.4% ►0.0% 4 Agricultural Marketing Section Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Uganda (wholesale) 1,750 1,900 470 500 Uganda (retail) 1,900 2,185 820 900 Source: https://farmgainafrica.org/ Date 10th September, 2021 Table 4A: Tobacco sales for 2021/22 trade season ending 22nd August, 2021 Tobacco type Company Contracted volume (kg) EVALUATED MARKETS (CUMMULATIVE) BELO KG Value (USD) Average price (USD/KG) VFC Alliance One Tobacco Tanzania Ltd 20,040,000 410,978 18,857,569 28,307,316 1.50 JTI Leaf Services Ltd 14,460,000 297,865 14,074,601 24,034,375 1.71 Premium Active Tanzania Ltd 16,000,000 287,194 13,488,012 20,740,505 1.54 Pachtec Company Ltd 4,461,838 57,284 2,609,127 3,627,934 1.39 Mo Green International Company Limited 2,800,000 36,017 1,677,408 2,545,870 1.52 Naile Leaf (T) Co. Ltd 2,535,000 43,336 1,851,806 2,775,124 1.50 Grand Tobacco Limited 1,815,000 Magefa Growers Ltd 4,400,000 40,179 1,755,989 2,414,887 1.38 Jespan Company Ltd 760,000 9,865 486,296 661,391 1.36 ENV Services Ltd 800,000 7,932 360,503 520,860 1.44 Biexen Company Limited 29,145 717 29,145 40,041 1.37 SUB TOTAL VFC 68,071,838 1,191,367 55,190,456 85,668,303 1.55 DFC Premium Active Tanzania Ltd 500,000 11,022 531,792 698,768 1.31 GRAND TOTAL 68,571,838 1,202,389 55,722,247 86,367,071 1.55 5 Agricultural Marketing Section Table 4B: Tobacco sales (Regions) for 2021/2022 trade season ending 22nd August, 2021 Region Contracted Volume Amount Bought Belo Kilograms Katavi 7,350,000 141,785 6,229,618 Mbeya 10,280,000 167,660 8,067,643 Songwe 670,000 12,953 624,655 Kigoma 4,705,000 93,825 4,194,878 Tabora 32,546,838 575,479 26,682,758 Shinyanga 9,930,000 162,321 7,648,131 Geita 1,070,000 17,809 871,093 Kagera 70,000 1,180 58,557 Iringa 200,000 2,446 157,631 Singida 1,250,000 15,909 655,493 Total VFC 68,071,838 1,191,367 55,190,456 Ruvuma (DFC) 500,000 11,022 531,791.96 Total (DFC+VFC) 68,571,838 1,202,389 55,722,247 Table 5: Cotton sales for 2021/22 trade season ending 29th August, 2021 SALES TRANSPOTATION Previous weeks (KGS) Current week (KGS) Total (KGS) Previous weeks (KGS) Current week (KGS) Total (KGS) 133,918,964 4,563,526 138, 482,490 133,918,964 4,563,526 138, 482,490 Source: Tanzania Cotton Board, 2021 6 Agricultural Marketing Section Table 6: Pigeon peas sales for 2021/22 season as of 25th August 2021 Date District AMCOS Company Amount (Kg) TZS/Kg TZS 11/08/2021 Namtumbo Ushirika B Afrisian Ginning 41,428 1,280 53,027,840 12/08/2021 Tunduru Mtetesi MeTL 150,000 1,300 195,000,000 Mtetesi RBST 210,251 1,290 271,223,790 18/08/2021 Namtumbo Ushirika B MeTL 206,517 1,320 272,602,440 Ushirika B LENIC 200,000 1,320 264,000,000 19/08/2021 Tunduru Asema LENIC 300,000 1,344 403,200,000 Asema LENIC 845,498 1,340 1,132,967,320 25/08/2021 Namtumbo Namtumbo LENIC 819,149 1,380 1,130,425,620 02/09/2021 Tunduru Naluwale MeTL 280,000 1,203 336,840,000 Naluwale LENIC 519,689 1,200 623,626,800 GRAND TOTAL 3,572,532 4,682,913,810 Table 7: Planting and harvesting time for maximum market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 7 Agricultural Marketing Section Important updates ✓ On August, the Government of Tanzania has provided a total of TZS 19 billion to the National Food Reserve Agency (NFRA) and Cereals and Other Produce Board (CPB) for the purchase of maize. ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace ✓ Weather Forecast October-December 2021. o Vuli rains are expected to be below normal to normal and characterized by prolonged periods of dry spells. o The Vuli rainy season is expected to have a poor start in the third and fourth weeks of October 2021 with poor distribution in many areas. o Besides the below normal to normal rainfall condition, warmer than usual temperatures are expected across bimodal areas during the Vuli rainy season. o For more information visit |Tanzania Meteorological Authority Contacts Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122

# Extracted Content 1 Market Intelligence Unit United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section WEEKLY MARKET BULLETIN September 13-17, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week September 06-10 Current week September 13-17 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 40,000 128,300 155,400 81,400 102,300 140,900 56,300 Previous 42,700 136,600 164,800 91,800 116,000 154,000 61,700 Change ▼6.8% ▼6.5% ▼6.0% ▼12.8% ▼13.4% ▼9.3% ▼9.6% Key Messages Overall, prices of the main staple food commodities slightly decreased compared to their levels a week earlier with variations observed in spatial markets. Prices for bulrush millet, sorghum, round potatoes, finger millet, maize, rice, and beans decreased by 13, 13, 10, 9, 7, 7 and 6 percent respectively. Staple food prices are projected to trend seasonably due to the on-going May-to-August harvesting season which could result in increase in market supply and reduced market prices. Tobacco: Total sales were 68,571,838 kilograms with a total value of USD 86.4 mil for the week ended 22nd August. Cotton: Total sales were 142,537,273 kilograms week ended 12th September. Coffee: Total sales were 21,737,913 kilograms with a total value of USD 43.2 mil for the week ended 10th Sept, 2021 Pigeon peas: Total sales were 4,036,709,757 kilograms with a total value of TZS 4 billion for the week ended 16th September, 2021. 2 Market Intelligence Unit Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 39,800 145,000 185,000 49,300 50,000 120,000 52,500 Previous 38,700 142,500 185,000 50,200 49,500 122,500 62,000 Change ▲2.8% ▲1.7% ►0.0% ▼1.8% ▲1.0% ▼2.1% ▼18.1% Arusha Current 47,500 160,000 185,000 67,500 70,000 NA 42,500 Previous 47,000 160,000 155,000 67,500 70,000 NA 42,500 Change ▲1.1% ►0.0% ▲16.2% ►0.0% ►0.0% ►0.0% Dar es Salaam Current 52,300 152,500 212,500 87,500 82,500 157,500 58,500 Previous 50,300 155,000 211,700 85,000 80,000 157,500 52,300 Change ▲3.8% ▼1.6% ▲0.4% ▲2.9% ▲3.0% ►0.0% ▲10.6% Morogoro Current 40,500 170,000 180,000 120,000 120,000 160,000 70,000 Previous 39,500 170,000 180,000 120,000 120,000 160,000 70,000 Change ▲2.5% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tanga Current 44,000 145,000 160,000 85,000 100,000 170,000 44,500 Previous 44,000 145,000 165,000 85,000 100,000 170,000 45,000 Change ►0.0% ►0.0% ▼3.1% ►0.0% ►0.0% ►0.0% ▼1.1% Ruvuma Current 26,800 170,000 125,000 NA NA NA 73,000 Previous 26,000 170,000 120,000 NA NA NA 73,500 Change ▲3.0% ►0.0% ▲4.0% ▼0.7% Iringa Current 32,500 160,000 165,000 90,000 NA 150,000 65,000 Previous 34,000 160,000 145,000 90,000 NA 150,000 50,000 Change ▼4.6% ►0.0% ▲12.1% ►0.0% ►0.0% ▲23.1% Njombe Current 36,500 145,000 152,000 NA NA 125,500 31,300 Previous wee 36,500 145,000 152,000 NA NA 125,500 31,300 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Rukwa Current 31,300 105,000 167,500 NA NA 137,500 65,000 Previous 31,500 105,000 170,000 NA NA 175,000 65,000 Change ▼0.6% ►0.0% ▼1.5% ▼27.3% ►0.0% 3 Market Intelligence Unit Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Kagera Current 56,300 128,800 127,500 105,000 130,000 147,500 57,500 Previous 55,000 130,000 130,000 125,000 155,000 155,000 60,000 Change ▲2.3% ▼0.9% ▼2.0% ▼19.0% ▼19.2% ▼5.1% ▼4.3% Mara Current 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Previous 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Manyara Current 43,300 142,500 135,000 70,000 90,000 125,000 55,000 Previous 45,000 150,000 135,000 70,000 90,000 125,000 65,000 Change ▼3.9% ▼5.3% ►0.0% ►0.0% ►0.0% ►0.0% ▼18.2% Shinyanga Current 43,500 110,000 175,000 115,000 105,000 115,000 85,000 Previous 43,500 110,000 175,000 115,000 105,000 115,000 85,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Mtwara Current 40,000 145,000 167,500 57,500 NA 180,000 57,500 Previous 40,000 145,000 167,500 57,500 NA 180,000 57,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tabora Current 35,500 102,500 170,000 145,000 NA 175,000 62,500 Previous 35,500 102,500 170,000 145,000 NA 175,000 62,500 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Kigoma Current 47,200 95,000 140,000 85,000 85,000 160,000 45,000 Previous 47,200 93,500 125,000 80,000 NA 145,000 55,000 Change ►0.0% ▲1.6% ▲10.7% ▲5.9% ▲9.4% ▼22.2% 4 Market Intelligence Unit Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Uganda (wholesale) 1,839 1,970 525 492 Uganda (retail) 1,970 2,298 854 919 Source: https://farmgainafrica.org/ Date 17th Septemba, 2021 Table 4A: Tobacco sales for 2021/22 trade season ending 22nd August, 2021 Tobacco type Company Contracted volume (kg) EVALUATED MARKETS (CUMMULATIVE) BELO KG Value (USD) Average price (USD/KG) VFC Alliance One Tobacco Tanzania Ltd 20,040,000 410,978 18,857,569 28,307,316 1.50 JTI Leaf Services Ltd 14,460,000 297,865 14,074,601 24,034,375 1.71 Premium Active Tanzania Ltd 16,000,000 287,194 13,488,012 20,740,505 1.54 Pachtec Company Ltd 4,461,838 57,284 2,609,127 3,627,934 1.39 Mo Green International Company Limited 2,800,000 36,017 1,677,408 2,545,870 1.52 Naile Leaf (T) Co. Ltd 2,535,000 43,336 1,851,806 2,775,124 1.50 Grand Tobacco Limited 1,815,000 Magefa Growers Ltd 4,400,000 40,179 1,755,989 2,414,887 1.38 Jespan Company Ltd 760,000 9,865 486,296 661,391 1.36 ENV Services Ltd 800,000 7,932 360,503 520,860 1.44 Biexen Company Limited 29,145 717 29,145 40,041 1.37 SUB TOTAL VFC 68,071,838 1,191,367 55,190,456 85,668,303 1.55 DFC Premium Active Tanzania Ltd 500,000 11,022 531,792 698,768 1.31 GRAND TOTAL 68,571,838 1,202,389 55,722,247 86,367,071 1.55 5 Market Intelligence Unit Table 4B: Tobacco sales (Regions) for 2021/2022 trade season ending 22nd August, 2021 Region Contracted Volume Amount Bought Belo Kilograms Katavi 7,350,000 141,785 6,229,618 Mbeya 10,280,000 167,660 8,067,643 Songwe 670,000 12,953 624,655 Kigoma 4,705,000 93,825 4,194,878 Tabora 32,546,838 575,479 26,682,758 Shinyanga 9,930,000 162,321 7,648,131 Geita 1,070,000 17,809 871,093 Kagera 70,000 1,180 58,557 Iringa 200,000 2,446 157,631 Singida 1,250,000 15,909 655,493 Total VFC 68,071,838 1,191,367 55,190,456 Ruvuma (DFC) 500,000 11,022 531,791.96 Total (DFC+VFC) 68,571,838 1,202,389 55,722,247 Table 5: Cotton sales for 2021/22 trade season week no. 18 ending 12th September, 2021 SALES TRANSPOTATION Previous weeks (KGS) Current week (KGS) Total (KGS) Previous weeks (KGS) Current week (KGS) Total (KGS) 141,371,266 1,166,007 142,537,273 141,371,266 1,262,536 142,633,802 Source: Tanzania Cotton Board, 2021 6 Market Intelligence Unit Table 6: Pigeon peas sales for 2021/22 season as of 16th September, 2021 Date District AMCOS Company Amount (Kg) TZS/Kg TZS 11/08/2021 Namtumbo Ushirika B Afrisian Ginning 41,428 1,280 53,027,840 12/08/2021 Tunduru Mtetesi MeTL 150,000 1,300 195,000,000 Tunduru Mtetesi RBST 210,251 1,290 271,223,790 18/08/2021 Namtumbo Ushirika B MeTL 206,517 1,320 272,602,440 Namtumbo Ushirika B LENIC 200,000 1,320 264,000,000 19/08/2021 Tunduru Asema LENIC 300,000 1,344 403,200,000 Tunduru Asema LENIC 845,498 1,340 1,132,967,320 25/08/2021 Namtumbo Namtumbo LENIC 819,149 1,380 1,083,734,127 16/09/2021 Tunduru Mruji LENIC 325,184 1,110 360,954,240 GRAND TOTAL 3,098,027 4,036,709,757 Table 7: Coffee sales (by varieties) for 2021/22 trade season ending 10th Sept, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 4,197,431 14,886,829 722,118 2,817,919 4,919,549 17,704,748 Hard Arabika 96,420 208,218 96,420 208,218 Robusta 16,721,944 25,313,689 16,721,944 25,313,689 Total 4,197,431 14,886,829 17,540,482 28,339,827 21,737,913 43,226,655 Source: Tanzania Coffee Board, 2021 7 Market Intelligence Unit Table 8: Planting and harvesting time for maximum market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 Important updates ✓ This week, the Government of Tanzania has provided a total of TZS 15 billion to the National Food Reserve Agency (NFRA) for the purchase of maize in the Southern Highlands regions. ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace ✓ Weather Forecast October-December 2021. o Vuli rains are expected to be below normal to normal and characterized by prolonged periods of dry spells. o The Vuli rainy season is expected to have a poor start in the third and fourth weeks of October 2021 with poor distribution in many areas. o Besides the below normal to normal rainfall condition, warmer than usual temperatures are expected across bimodal areas during the Vuli rainy season. o For more information visit |Tanzania Meteorological Authority Contacts Ag: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section WEEKLY MARKET BULLETIN September 20-24, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Previous week September 13-17 Current week September 20-24 National Average Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Current 43,100 127,600 161,600 87,200 101,600 152,200 59,800 Previous 42,500 135,800 165,100 87,600 102,300 150,900 61,700 Change ▲1.4% ▼6.4% ▼2.2% ▼0.5% ▼0.7% ▲0.9% ▼3.2% Key Messages Overall, the average prices of food crops slightly decreased compared to their levels a week earlier (table 1). Some variations in the prices of crops were observed across the markets (Table2). While prices for rice, round potato, beans, bulrush millet and sorghum eased by 6.4%, 3.2%, 2.2%, 0.7%, and 0.5% respectively, those for maize and finger millet slightly increased by 1.4% and 0.9% respectively. Cotton: Total sales were 144,011,169 kilograms for the week ended 19th September. Coffee: Total sales were 21,737,913 kilograms with a total value of USD 43.2 mil for the week ended 10th Sept, 2021 Pigeon peas: Total sales were 3,169,976 kilograms with a total value of TZS 4.2 billion for the week ended 22nd September, 2021. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 44,500 144,300 185,300 49,000 53,500 121,300 53,000 Previous 39,800 145,000 185,000 49,300 50,000 120,000 52,500 Change ▲10.6% ▼0.5% ▲0.2% ▼0.6% ▲6.5% ▲1.1% ▲0.9% Arusha Current 47,000 162,500 155,000 62,500 72,500 NA 42,500 Previous 47,500 160,000 185,000 67,500 70,000 NA 42,500 Change ▼1.1% ▲1.5% ▼19.4% ▼8.0% ▲3.4% ►0.0% Dar es Salaam Current 49,300 151,700 201,700 87,500 82,500 162,500 55,000 Previous 52,300 152,500 212,500 87,500 82,500 157,500 58,500 Change ▼6.1% ▼0.5% ▼5.4% ►0.0% ►0.0% ▲3.1% ▼6.4% Morogoro Current 40,500 170,000 180,000 120,000 120,000 160,000 70,000 Previous 40,500 170,000 180,000 120,000 120,000 160,000 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Tanga Current 46,000 120,000 150,000 85,000 100,000 170,000 45,000 Previous 44,000 145,000 160,000 85,000 100,000 170,000 44,500 Change ▲4.3% ▼20.8% ▼6.7% ►0.0% ►0.0% ►0.0% ▲1.1% Ruvuma Current 26,500 170,000 140,000 NA NA NA 75,000 Previous 26,800 170,000 125,000 NA NA NA 73,000 Change ▼1.1% ►0.0% ▲10.7% ▲2.7% Iringa Current 32,300 160,000 162,500 90,000 NA 135,000 55,000 Previous 32,500 160,000 165,000 90,000 NA 150,000 65,000 Change ▼0.6% ►0.0% ▼1.5% ►0.0% ▼11.1% ▼18.2% Rukwa Current 31,500 100,000 166,300 NA NA 140,000 60,000 Previous 31,300 105,000 167,500 NA NA 137,500 65,000 Change ▲0.6% ▼5.0% ▼0.7% ▲1.8% ▼8.3% 3 Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Kagera Current 57,500 125,000 125,000 86,300 105,000 155,000 57,500 Previous 56,300 128,800 127,500 105,000 130,000 147,500 57,500 Change ▲2.1% ▼3.0% ▼2.0% ▼21.7% ▼23.8% ▲4.8% ►0.0% Mara Current 62,500 105,000 175,000 70,000 190,000 190,000 91,300 Previous 62,500 105,000 195,000 62,500 190,000 190,000 91,300 Change ►0.0% ►0.0% ▼11.4% ▲10.7% ►0.0% ►0.0% ►0.0% Manyara Current 43,000 150,000 135,000 70,000 90,000 125,000 47,500 Previous 43,300 142,500 135,000 70,000 90,000 125,000 55,000 Change ▼0.7% ▲5.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼15.8% Shinyanga Current 43,500 125,000 175,000 115,000 105,000 115,000 85,000 Previous 43,500 110,000 175,000 115,000 105,000 115,000 85,000 Change ►0.0% ▲12.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Mtwara Current 40,000 155,000 165,000 58,800 NA 180,000 57,500 Previous 40,000 145,000 167,500 57,500 NA 180,000 57,500 Change ►0.0% ▲6.5% ▼1.5% ▲2.2% ►0.0% ►0.0% Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) Tabora Current 37,300 102,500 170,000 145,000 NA 175,000 57,500 Previous 35,500 102,500 170,000 145,000 NA 175,000 62,500 Change ▲4.8% ►0.0% ►0.0% ►0.0% ►0.0% ▼8.7% Kigoma Current 44,600 100,000 138,800 95,000 97,500 150,000 45,000 Previous 47,200 95,000 140,000 85,000 85,000 160,000 45,000 Change ▼5.8% ▲5.0% ▼0.9% ▲10.5% ▲12.8% ▼6.7% ►0.0% 4 Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,844 1,975 492 527 Retail 1,975 2,305 856 922 Source: https://farmgainafrica.org/ Date 24th September, 2021 Table 5: Cotton sales for 2021/22 trade season week no. 19 ending 19th September, 2021 Sales Transportation Previous weeks (KGS) Current week (KGS) Total (KGS) Previous weeks (KGS) Current week (KGS) Total (KGS) 142,726,153 1,285,016 144,011,169 142,822,682 612,315 143,434,997 Source: Tanzania Cotton Board, 2021 Table 6: Pigeon peas sales for 2021/22 season as of 22nd September, 2021 Date District AMCOS Company Amount (Kg) TZS/Kg TZS 11/08/2021 Namtumbo Ushirika B Afrisian Ginning 41,428 1,280 53,027,840 12/08/2021 Tunduru Mtetesi MeTL 150,000 1,300 195,000,000 Tunduru Mtetesi RBST 210,251 1,290 271,223,790 18/08/2021 Namtumbo Ushirika B MeTL 206,517 1,320 272,602,440 Namtumbo Ushirika B LENIC 200,000 1,320 264,000,000 19/08/2021 Tunduru Asema LENIC 300,000 1,344 403,200,000 Tunduru Asema LENIC 845,498 1,340 1,132,967,320 25/08/2021 Namtumbo Namtumbo LENIC 819,149 1,380 1,083,734,127 16/09/2021 Tunduru Mruji LENIC 325,184 1,110 360,954,240 22/09/2021 Namtumbo Ushirika B LENIC 71,949 990 71,229,510 GRAND TOTAL 3,169,976 4,154,630,760 5 Table 7: Coffee sales (by varieties) for 2021/22 trade season ending 10th Sept, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 4,197,431 14,886,829 722,118 2,817,919 4,919,549 17,704,748 Hard Arabika 96,420 208,218 96,420 208,218 Robusta 16,721,944 25,313,689 16,721,944 25,313,689 Total 4,197,431 14,886,829 17,540,482 28,339,827 21,737,913 43,226,655 Source: Tanzania Coffee Board, 2021 Table 8: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace ✓ Weather Forecast October-December 2021. o Vuli rains are expected to be below normal to normal and characterized by prolonged periods of dry spells. o The Vuli rainy season is expected to have a poor start in the third and fourth weeks of October 2021 with poor distribution in many areas. o Besides the below normal to normal rainfall condition, warmer than usual temperatures are expected across bimodal areas during the Vuli rainy season. o For more information visit |Tanzania Meteorological Authority 6 Contacts Ag: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122

# Extracted Content 1 United Republic of Tanzania Ministry of Agriculture Agricultural Marketing Section WEEKLY MARKET BULLETIN September 27-October 01, 2021 Table 1: National weekly average wholesale prices (TZS/100 kg bag) Key Messages Overall, the National average wholesale prices of major food crops increased compared to their levels a week earlier (table 1). Some variations in the prices of crops were observed across the markets (Table2). Prices for maize, rice, beans, sorghum, bulrush millet and finger millet increased by 2.7%, 4.7%, 1.2%, 4.6%, 6.3% and 1.9% respectively. On the other hand, price of round potatoes declined by 4.3%. Cotton: Total sales were 144,011,169 kilograms for the week ended 19th September. Coffee: Total sales were 26,653,288 kilograms with a total value of USD 57.9 mil for the week ended 24th Sept, 2021 Pigeon peas: Total sales were 3,169,976 kilograms with a total value of TZS 4.2 billion for the week ended 22nd September, 2021. 2 Table 2: Regional weekly average wholesale market prices (TZS/100 kg bag) Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Dodoma Current 43,500 140,000 185,000 50,000 54,500 121,500 53,300 Previous 44,500 144,300 185,300 49,000 53,500 121,300 53,000 Change ▼2.3% ▼3.1% ▼0.2% ▲2.0% ▲1.8% ▲0.2% ▲0.6% Arusha Current 47,500 162,500 160,000 63,500 72,500 132,500 42,500 Previous 47,000 162,500 155,000 62,500 72,500 NA 42,500 Change ▲1.1% ►0.0% ▲3.1% ▲1.6% ►0.0% ►0.0% Dar es Salaam Current 51,800 166,700 205,000 87,500 82,500 162,500 55,200 Previous 49,300 151,700 201,700 87,500 82,500 162,500 55,000 Change ▲4.8% ▲9.0% ▲1.6% ►0.0% ►0.0% ►0.0% ▲0.4% Tanga Current 50,000 120,000 150,000 85,000 100,000 170,000 43,000 Previous 46,000 120,000 150,000 85,000 100,000 170,000 45,000 Change ▲8.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ▼4.7% Ruvuma Current 27,500 170,000 140,000 NA NA NA 73,500 Previous 26,500 170,000 140,000 NA NA NA 75,000 Change ▲3.6% ►0.0% ►0.0% ▼2.0% Iringa Current 32,000 160,000 145,000 90,000 NA 150,000 45,000 Previous 32,300 160,000 162,500 90,000 NA 135,000 55,000 Change ▼0.9% ►0.0% ▼12.1% ►0.0% ▲10.0% ▼22.2% Rukwa Current 31,000 112,500 147,500 NA NA 125,000 52,500 Previous 31,500 100,000 166,300 NA NA 140,000 60,000 Change ▼1.6% ▲11.1% ▼12.7% ▼12.0% ▼14.3% 3 Kigoma Current 47,200 95,000 137,500 90,000 85,000 145,000 45,000 Previous 44,600 100,000 138,800 95,000 97,500 150,000 45,000 Change ▲5.5% ▼5.3% ▼0.9% ▼5.6% ▼14.7% ▼3.4% ►0.0% Tabora Current 39,000 102,500 170,000 145,000 NA 175,000 52,500 Previous 37,300 102,500 170,000 145,000 NA 175,000 52,500 Change ▲4.4% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Region Week Maize Rice Beans Sorghum Bulrush millet Finger millet Round Potato Kagera Current 58,000 127,000 130,000 87,500 105,000 155,000 57,500 Previous 57,500 125,000 125,000 86,300 105,000 155,000 57,500 Change ▲0.9% ▲1.6% ▲3.8% ▲1.4% ►0.0% ►0.0% ►0.0% Mara Current 62,500 105,000 195,000 62,500 190,000 190,000 92,500 Previous 62,500 105,000 175,000 70,000 190,000 190,000 91,300 Change ►0.0% ►0.0% ▲10.3% ▼12.0% ►0.0% ►0.0% ▲1.3% Manyara Current 46,000 150,000 135,000 74,000 90,000 125,000 52,000 Previous 43,000 150,000 135,000 70,000 90,000 125,000 47,500 Change ▲6.5% ►0.0% ►0.0% ▲5.4% ►0.0% ►0.0% ▲8.7% Shinyanga Current 45,500 125,000 175,000 125,000 125,000 135,000 85,000 Previous 43,500 125,000 175,000 115,000 105,000 115,000 85,000 Change ▲4.4% ►0.0% ►0.0% ▲8.0% ▲16.0% ▲14.8% ►0.0% Mtwara Current 40,000 165,000 162,500 60,000 NA 180,000 57,500 Previous 40,000 155,000 165,000 58,800 NA 180,000 57,500 Change ►0.0% ▲6.1% ▼1.5% ▲2.0% ►0.0% ►0.0% Kilimanjaro Current 52,500 175,000 150,000 120,000 140,000 NA 70,000 Previous 52,500 175,000 150,000 120,000 140,000 NA 70,000 Change ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% ►0.0% Notes: ✓ Unit of measurement: for food crops are in TZS per 100kg. ✓ Commodity prices varies according to quality and variety. ✓ The symbols (▲▼►) depict the direction of price changes. (▲) price increased; (▼) price decreased; (►) no change or changes smaller than one percent. ✓ The indicative prices for fertilizers are retail and vary from region to region depending on the distance. ✓ N/A - data not available. Source: Ministry of Agriculture (MoA) in Collaboration with the Ministry of Industry and Trade (MIT) 4 Table 3: Prices for rice and maize in Uganda (TZS/kg) Rice Maize Min. price Max. price Min. price Max. price Wholesale 1,831 1,961 490 523 Retail 1,961 2,288 850 915 Source: https://farmgainafrica.org/ Date 01st October, 2021 Table 5: Cotton sales for 2021/22 trade season week no. 19 ending 19th September, 2021 Sales Transportation Previous weeks (KGS) Current week (KGS) Total (KGS) Previous weeks (KGS) Current week (KGS) Total (KGS) 142,726,153 1,285,016 144,011,169 142,822,682 612,315 143,434,997 Source: Tanzania Cotton Board, 2021 Table 6: Pigeon peas sales for 2021/22 season as of 22nd September, 2021 Date District AMCOS Company Amount (Kg) TZS/Kg TZS 11/08/2021 Namtumbo Ushirika B Afrisian Ginning 41,428 1,280 53,027,840 12/08/2021 Tunduru Mtetesi MeTL 150,000 1,300 195,000,000 Tunduru Mtetesi RBST 210,251 1,290 271,223,790 18/08/2021 Namtumbo Ushirika B MeTL 206,517 1,320 272,602,440 Namtumbo Ushirika B LENIC 200,000 1,320 264,000,000 19/08/2021 Tunduru Asema LENIC 300,000 1,344 403,200,000 Tunduru Asema LENIC 845,498 1,340 1,132,967,320 25/08/2021 Namtumbo Namtumbo LENIC 819,149 1,380 1,083,734,127 16/09/2021 Tunduru Mruji LENIC 325,184 1,110 360,954,240 22/09/2021 Namtumbo Ushirika B LENIC 71,949 990 71,229,510 GRAND TOTAL 3,169,976 4,154,630,760 5 Table 7: Coffee sales (by varieties) for 2021/22 trade season ending 24th Sept, 2021 Type of Coffee Auction Direct exports Local roast Total KGS USD KGS USD KGS USD KGS USD Mild Arabika 6,747,280 24,006,847 1,259,628 5,052,981 - - 8,006,908 29,059,828.45 Hard Arabika - - 169,860 425,727 - - 169,860 425,727.12 Robusta - - 18,191,344 27,969,485 285,176 408,441.68 18,476,520 28,377,926.76 Total 6,747,280 24,006,847 19,620,832 33,448,193 285,176 408,441.68 26,653,288 57,863,482.33 Source: Tanzania Coffee Board, 2021 Table 8: Planting and harvesting time for better market price for horticultural crops Commodity The appropriate harvesting time The inappropriate harvesting time Proper time for planting Onions Feb- July July- Nov Sept- Dec Tomatoes Jan- May Jun- Dec Sept- Dec Green pepper Feb- Apr. Jun- Jan. Oct – Nov Carrot Oct- Mar. Apr- Sept July- Oct Cucumber Feb- May May- Jan Dec- Jan Round potatoes March- Jun Jul- Jan Dec- Feb Watermelon Mar- Apr. Oct-Dec May- Sept, Dec- Feb Jan- Feb, Aug- Sept Sweet pepper Jun- Nov Dec- May Feb- March Ginger Apr- Jul Aug - Mar Dec- Feb Source: TAHA, 2020 Important updates ✓ The Ministry of Agriculture has established an online market platform (M-Kilimo) to facilitate market access by farmers and traders. Visit M-Kilimo - MarketPlace ✓ Weather Forecast October-December 2021. o Vuli rains are expected to be below normal to normal and characterized by prolonged periods of dry spells. o The Vuli rainy season is expected to have a poor start in the third and fourth weeks of October 2021 with poor distribution in many areas. o Besides the below normal to normal rainfall condition, warmer than usual temperatures are expected across bimodal areas during the Vuli rainy season. o For more information visit |Tanzania Meteorological Authority 6 Contacts Ag: Assistant Director, Agricultural Marketing Section, P.O. Box 2182, DODOMA. Email: [email protected] Mobile: +255 686 107 673 / +255 713 309 122

# Extracted Content CONTENTS iii The Revolutionary Government of Zanzibar NATIONAL SAMPLE CENSUS OF AGRICULTURE 2007/2008 VOLUME VI: LIVESTOCK SECTOR -ZANZIBAR REPORT Executed jointly by the Office of the Chief Government Statistician, National Bureau of Statistics, Ministries of Agriculture and Natural Resources and Livestock and Fishery in Zanzibar. January 2012 The Revolutionary Government of Zanzibar NATIONAL SAMPLE CENSUS OF AGRICULTURE 2007/2008 SMALL HOLDER AGRICULTURE VOLUME VI: LIVESTOCK SECTOR -ZANZIBAR REPORT Executed jointly by the Office of the Chief Government Statistician, National Bureau of Statistics. Ministries of Agriculture and Natural Resources and Livestock and Fishery in Zanzibar. January, 2012 CONTENTS Tanzania Agriculture Sample Census - 2007/08 i TABLE OF CONTENTS ABBREVIATIONS ......................................................................................................................... iii PREFACE ...................................................................................................................................... iv EXECUTIVE SUMMARY .............................................................................................................. vi LIST OF TABLES ......................................................................................................................... viii LIST OF MAPS ................................................................................................................................. x 1.0 BACKGROUND INFORMATION .............................................................................. 1 2.0 INTRODUCTION ......................................................................................................... 1 2.1 Rationale for Conducting the National Sample Census of Agriculture ......................... 1 2.2 Census Objectives .......................................................................................................... 2 2.3 Census Coverage ........................................................................................................... 2 2.3.1 Census Scope ................................................................................................................ 3 2.3.2 Main Activities Undertaken .......................................................................................... 4 2.4 Census Methodology ..................................................................................................... 4 2.4.1 Census Organization ..................................................................................................... 4 2.4.2 Tabulation Plan Preparation ......................................................................................... 5 2.4.3 Sample Design .............................................................................................................. 5 2.4.4 Questionnaire Design and Other Census Instruments .................................................. 6 2.4.5 Field Pilot-Testing ........................................................................................................ 7 2.4.6 Training of Trainers, Supervisors and Enumerators ..................................................... 7 2.4.7 Information, Education and Communication (IEC) Campaign .................................... 7 2.4.8 Data Collection ............................................................................................................. 7 2.4.9 Field Supervision and Consistency Checks .................................................................. 8 2.4.10 Data Processing and Analysis ....................................................................................... 8 2.4.11 Data Entry ..................................................................................................................... 8 2.4.12 Batch Validation ........................................................................................................... 9 2.4.13 Tabulations ................................................................................................................... 9 2.4.14 Analysis and Report Preparation .................................................................................. 9 2.4.15 Data Quality Control ..................................................................................................... 9 2.5 Funding Arrangements .................................................................................................. 9 3.1 Livestock Population and Growth ............................................................................... 10 3.1.1 Cattle Population ......................................................................................................... 11 3.1.2 Goat Population ........................................................................................................... 14 CONTENTS Tanzania Agriculture Sample Census - 2007/08 ii 3.1.3 Sheep Population ......................................................................................................... 19 3.1.4 Pig Population .............................................................................................................. 21 3.1.5 Chicken Population ...................................................................................................... 21 3.1.6 Other Livestock ........................................................................................................... 28 3.2 Livestock products -Milk Production .......................................................................... 29 3.3 Contribution of Livestock to Crop production ............................................................ 32 3.4 Livestock Diseases and control ................................................................................... 32 3.4.1 Common Livestock Diseases ....................................................................................... 32 3.4.2 Livestock Disease Control Methods ........................................................................... 39 3.4.3 Deworming Practices .................................................................................................. 43 3.5 Bee Keeping ................................................................................................................ 46 3.5.1 Beehives by Type of Bees .......................................................................................... 47 3.5.2 Quantity of Honey Harvested and Average Prices ..................................................... 48 3.5.3 Honey Outlets by Location and Region ...................................................................... 49 3.6 Access to Extension Services by District ................................................................... 50 3.6.1 Sources of Extension Services .................................................................................... 50 3.6.2 Extension Advice by Type of Messages ..................................................................... 51 3.6.3 Number of Households which Received Advice Messages on Disease Control ........ 51 CONCLUSIONS ............................................................................................................................. 52 5. APPENDICES ............................................................................................................................. 57 ABBREVIATIONS Tanzania Agriculture Sample Census - 2007/08 iii ABBREVIATIONS ASDP Agricultural Sector Development Project CSPro Census and Survey Processing Program DFID Department For International Development DIAS District Integrated Agricultural Survey DS District Supervisor EAs Enumeration Areas EU European Union GDP Gross Domestic Product Ha Hectares IAS Integrated Agricultural Survey ICR Intelligent Character Recognition IEC Information, Education and Communication JICA Japanese International Cooperation Agency LRHH Livestock Raising Households MANR Ministry of Agriculture and Natural Resources MCM Ministry of Co-operatives and Marketing MWLD Ministry of Water and Livestock Development NBS National Bureau of Statistics NGO Non Governmental Organization NMS National Master Sample NSCA National Sample Census of Agriculture NSGRP National Strategy for Growth and Reduction of Poverty OCGS Office of the Chief Government Statistician PORALG President’s Office, Regional Administration and Local Government PPS Probability Proportional to Size PSU Primary Sampling Unit RAAS Rapid Appraisal Agricultural Survey RSM Regional Statistical Manager SPSS Statistical Package for Social Science TOT Training of Trainers UNDP United Nations Development Programme UNFAO United Nations Food and Agricultural Organization VPO Vice President Office PREFACE Tanzania Agriculture Sample Census - 2007/08 iv PREFACE At the end of the 2007/08 Agricultural Year, the Office of the Chief Government Statistician, (OCGS) in collaboration with National Bureau of Statistics (NBS) and Ministries of Agriculture and Natural Resources; Livestock and Fisheries conducted the 2007/08 Agricultural Sample Census. This is the second Sample Census of Agriculture to be carried out in Zanzibar, the first one was conducted in 2002/03 Agricultural year. It is considered that this census is one of the largest to be carried out in Africa and indeed, in many other countries of the world. The census collected detailed data on crop production, crop marketing, crop storage, livestock production, fish farming, and poverty indicators. In addition to this, the census was large in its scope and coverage as it provides data that can be disaggregated at district level and thus allow comparisons with the 2002/03 National Sample Census of Agriculture. The census covered smallholders in rural areas only and all the large scale farms. This report provide the results of the small holder farming in livestock sector. The results presented in this report are detailed data on cattle, goats, pigs, sheep, chicken and other livestock. The extensive nature of the census in relation to its scope and coverage is a result of the increasing demand for more detailed information that can assist in the proper planning of the agricultural sector and in the administrative decentralization of planning to district level. It is hoped that this report will provide new insights for planners, policy makers, researchers and others involved in the agricultural sector in order to improve the prevailing conditions faced by agricultural households in the country. Furthermore, the report will provide deeper understanding on the procedures and techniques applied in carrying out the census. On behalf of the Government of Tanzania Zanzibar, I wish to express my appreciation for the financial support provided by the development partners, in particular, the Department for International Development (DFID) and the Japanese Government through the Japan International Cooperation Agency (JICA) and others who contributed through the pooled fund mechanism. My appreciation also goes to all those who in one-way or the other, have contributed to the success of the survey. In particular, I would also like to mention the enormous effort made by the Planning Group composed of professionals from the Agricultural and Environmental Statistics Section of the Office of Chief Government Statistician (OCGS), Agricultural Statistics Department of the National Bureau of Statistics (NBS), Ministry of Agriculture and Natural Resources, Ministry of PREFACE Tanzania Agriculture Sample Census - 2007/08 v Livestock and Fishery Zanzibar. Other are Ministry of Food Security and Cooperatives, Ministry of Livestock Development and Fisheries, Ministry of Water and Irrigation, the Prime Minister's Office, Regional Administration and Local Government, Ministry of Industries, Trade and Marketing in Tanzania Mainland and the Food and Agriculture Organization of the United Nations and the Censuses and Surveys Technical Working Group (CSTWG). Finally, I would like to extend my sincere gratitude to all professional staff of the Office of the Chief Government Statistician and National Bureau of Statistics, Mainland, the sector Ministries of Agriculture and Natural Resources the Consultants as well as Regional and District Supervisors and field enumerators for their commendable work. Certainly, without their dedication, the census would not have been such a success. Mr. Mohamed Hafith Rajab Chief Government Statistician Office of the Chief Government Statistician, Zanzibar Zanzibar EXECUTIVE SUMMARY Tanzania Agriculture Sample Census - 2007/08 vi EXECUTIVE SUMMARY Agriculture is an important economic sector of the Zanzibar economy in terms of food production, employment generation, production of raw materials for industry, and generation of foreign exchange earnings. The agricultural sector produces about 30.8 percent of GDP (Economic Survey, 2009) and the contribution of livestock was estimated to be 4.5 percent. In 2007, the Government of Tanzania launched the Agricultural Sample Census as an important part of the Poverty Monitoring Master plan which supports the production of statistics for advocacy of effective public policy, including poverty reduction, access to services, gender, as well as the standard production data normally collected in an agriculture census. The 2007/08 Agricultural Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, district, and National levels, rural development agencies, funding institutions, researchers, NGOs, farmers’ organizations, etc. This report provides detailed description of the state of the livestock sub-sector in Zanzibar for the agricultural year 2007/08. The detailed tabulations and analysis were based mainly on smallholder farms. In some cases, contribution of large scale farms is also included to give the overall Zanzibar estimates. The main types of livestock and poultry covered in the 2007/08 Agricultural Sample Census are cattle, goats, sheep, pigs, chicken, ducks, turkeys, rabbits, and donkeys. There was an equivalent of 170,715 livestock units in total representing a total of 228, 538 major livestock of different species. The goat livestock units were about 13,794, sheep were about 114.8 and pigs about 1,005 units. Chicken were kept by 60% of the households, while cattle were kept by 30% of the agricultural households. The trend shows that the number of goats increased by 31 percent, sheep by 18 percent and pigs by 10 percent per annum, while the number of cattle had declined by -0.9 percent between 2003 and 2008. The average number of cattle and goats per household were 4 and 9 respectively. Most of the cattle were kept in the Central district followed by Micheweni, Wete and West districts. However, Micheweni district had more cattle rearing households than the rest of the districts. Milk production from cows during the wet season was 115,021 liters (56%) and dropped to 87,490 litres (43%) during the dry season. Average milk production per cow was 2.5 litters during the wet season and 2.3 litres during the dry season. The number of milked cows also dropped from 44,718 during the wet season to 36,639 in the dry season. The price of milk was slightly higher than in the Mainland whereby the prices were Tshs. 508 in the wet season and increased to Tshs. 538 during the dry season. EXECUTIVE SUMMARY Tanzania Agriculture Sample Census - 2007/08 vii Regarding small ruminants, Central district has more households raising goats than any other district. About 4 percent of the agricultural households in Tanzania Zanzibar kept improved goats although, the number of improved goats was less than 20 percent. Sheep on the other hand are less important and only 574 households raised sheep most of which were found in the West disrict. With regard to chicken, over 90 percent of agricultural households raised chicken and a bigger proportion (21%) of chicken were kept in the West district and were dominated by the unimproved type (local). The trend shows that, the number of chicken has remained stagnant between 2003 and 2008. However, the number of layers has increased by 36 percent with an annual growth rate of about 7 percent, though there was a decline of 30 percent in the number of broilers. Apart from providing meat, milk and draught power, livestock supply organic fertilizers in terms of manure. In Zanzibar, a total of 6,806 households (7.7% of all households planting during Long rain) use organic fertilizers. Organic fertilizer was used on only 2,926 ha representing 7.8 percent of the total planted area during long rain season. Farm yard manure was used in all the districts but, was more common in the Central, South, North B and West. Mkoani and Chakechake were at the bottom in terms of organic fertiliser use. Livestock diseases have remained the most challenging constraint in the livestock sector. Common diseases affecting ruminants include Tick Borne Diseases (TBD), Tse- tse fly infestations, FMD and Lumpy skin Disease. Almost 50 percent of the cattle raising households encountered Tick Borne Diseases, and the problem was more serious in the Central district followed by Chake chake, Micheweni and Mkoani. Spraying with acaricides was the most common method used to control infections. Dipping and smearing were the commonest methods of tick control. For chicken, the Newcastle Disease and the Fowl Typhoid were reported to be a challenge in most of the agricultural households and only 10 percent of the households vaccinated their chicken against the Newcastle disease. Access to extension services varied between the districts and Micheweni district had the highest access (79%) followed by Chakechake(74%), Wete and Mkoani districts, each with (65%). North ‘A’, West and North ‘B’districts had less access to extension services. The government accounted for 50 percent of the extension services provided, other sources being NGOs/development projects, newspapers, radios, and televisions. ILLUSTRATIONS Tanzania Agriculture Sample Census - 2007/08 viii ILLUSTRATIONS List of Tables Table 3.1: Census Sample Size ........................................................................................................ 5 Table 3.2: Number of Livestock by Type ...................................................................................... 11 Table 3.3: Number of Households and Number of Cattle by Herd Size ....................................... 11 Table 3.4: Number of Households Raising Goats by Herd Size .................................................... 15 Table 3.5: Number of Households Raising Pigs by Herd Size ...................................................... 21 Table 3.6: Households Raising Chicken by Flock Size ................................................................. 25 Table 3.7: Households Raising Local Chicken by Flock Size ....................................................... 25 Table 3.8: Improved Chicken Population by Flock Size ............................................................... 28 Table 3.9: Population of Other Livestock by District .................................................................... 28 Table 3.10: Number of Households and Livestock by Type ........................................................... 29 Table 3. 11: Total Milk Production and Percentage by District ...................................................... 29 Table 3.12: Average Milk Production per Cow per day, by Category of Cow, Season and District ....................................................................................................... 30 Table 3.13: Average Cattle Milk price (Tshs/litre) per season by category of cow and District ... 30 Table 3.14 Animal Contribution to Crops: Number of Households and Planted Area by Organic Fertilizer use and District - Long Rainy Season ................. 32 Table 3.15 Number and Percentages of Livestock Raising Households practicing various Tick Control methods by District ................................................... 40 Table 3.16 Number and Percentages of Livestock Raising Households which Practiced Newcastle Control Methods by District ............................................. 41 Table 3.17: Number and Percentages of LRHH which Practiced Fowl Typhoid Control Methods by District ......................................................................................... 43 Table 3.18: Number of Goats/Sheep Rearing Households which Dewormed Goats/Sheep by District ................................................................................................ 45 Table 3.19 Number of Livestock Rearing Households which Dewormed Pigs by District ........... 45 Table 3.20 Number of Livestock Rearing Households which Dewormed Chicken by District ..... 46 Table 3.21: Number of Agricultural Households Involved in Honey Production by District ......... 47 Table 3.22: Number of Beehives by Type and District ................................................................... 48 Table 3.23: Quantity of Honey Harvested and Sold by Type of Bees and District ......................... 48 Table 3.24: Average Prices of Honey (Tshs /litre) by Type of Bees and by District ...................... 49 Table 3.25 Number of Agricultural Households by Location and Honey Outlets ........................ 51 Table 3.26 Percentage of Households which Received Extension Advice by Type of Message ... 51 ILLUSTRATIONS Tanzania Agriculture Sample Census - 2007/08 ix List of Charts Chart 3.1: Total Number of Livestock by Type…………………………………………... 10 Chart 3.2: Number of Households Keeping Livestock by Type………………………...... 10 Chart 3.3: Cattle Population and Average Head per Household by District……………… 12 Chart 3.4: Cattle Population Tend ……………………………………………………….. 12 Chart 3.5: Percentage of Households Rearing Indigenous Cattle by District…………….. 13 Chart 3.6: Percentage of Indigenous Cattle by District…………………………………… 13 Chart 3.7: Percentage of Households Rearing Improved Dairy Cattle by Districts………. 13 Chart 3.8: Percentage of Improved Dairy Cattle by District……………………………… 14 Chart 3.9: Dairy Cattle Population Trend………………………………………………… 14 Chart 3.10: Goats Population Trend……………………………………………………….. 14 Chart 3.11: Number and Percentage of Households Rearing Goats by District…………… 15 Chart 3.12 Number of Percentage of Goats by District…………………………………… 15 Chart 3.13: Number of Goats by Type and District…………………………………... 18 Chart 3.14: Percentage of Sheep Population by District…………………………………… 18 Chart 3.15: Sheep Population Trend……………………………………………………….. 19 Chart 3.16: Pigs Population Trend…………………………………………………………. 21 Chart 3.17: Number of Household Rearing Chicken by District…………………………... 21 Chart 3.18: Chicken Population by District………………………………………………... 23 Chart 3.19: Chicken Population trend………………………………………………………. 23 Chart 3.20: Indigenous Chicken Population Trend………………………………………… 25 Chart 3.21: Number of Indigenous Chicken……………………………………………….. 25 Chart 3.22 Improved of Chicken Population Trend…………………………………….…. 27 Chart 3.23 Percent and Number of Improved Chicken by District…………………..……. 27 Chart 3.24: Layers Population by District………………………………………………….. 27 Chart 3.25: Percent of HH Affected by Various Livestock Diseases…………………….… 33 Chart 3.26: Number and Percent of LRHH Encountering Lumpy Skin Diseases by District………………………………………………………………………….. 33 Chart 3.27: Number and Percent of LRHH Encountering Tick Problems by District………………………………………….........................................……. 35 Chart 3.28: Number and Percent of LRHH Encountering Foot and Mouth Disease by District …………………………………………………….………. 35 Chart 3.29: Number and Percent of LRHH Encountering Newcastle Problem by District………………………………………………………………………….. 35 Chart 3.30: Number and Percent of LRHH Encountering Fowl typhoid by District………. 35 Chart 3.31: Percent of HH reporting Newcastle Control by Methods…………………….... 40 Chart 3.31: Percent of LRHH Practicing Newcastle Control by District…………………... 41 Chart 3.32: Percent of LRHH reporting Fowl Typhoid Control methods…………...……... 42 Chart 3.33: Percent of LRHH Controlling Fowl Typhoid by District……………………… 42 Chart 3.34: Percentage of LRHH Deworming Livestock by District………………………. 43 Chart 3.35: Proportion of Households that Dewormed Cattle by District……………...…... 43 Chart 3.36: Proportion of HH that Dewormed Goats/Sheep by District………………..….. 44 Chart 3.37: Proportion of HH that Dewormed pigs by Districts………………………….... 44 Chart 3.38: Proportion of LRHH Deworming Chicken by Distrct…………………………. 46 Chart 3.39: Percent of Household Selling Honey by District………………………...…….. 50 Chart 3.40: Percentage of Households Receiving Livestock Extension Advise by District 50 Chart 3.41: Percent and distribution of Source of Extension Advice………………………. 50 Chart 3.42: Number of Households Receiving Extension Advice on Disease Control District…………………..…………………………………………….. 51 ILLUSTRATIONS Tanzania Agriculture Sample Census - 2007/08 x List of Maps Map 3.1 Zanzibar: Number of Cattle by District………………………………………... 16 Map 3.2 Zanzibar: Cattle Population per Sq. km……………………………………….. 16 Map 3.3 Zanzibar: Dairy Cattle Population by District …………………………….….. 17 Map 3.3 Zanzibar: Dairy Cattle Population by District………………………………… 17 Map 3.4 Zanzibar: Goat Population by District……………………………………….... 15 Map 3.5 Zanzibar: Goat Population by District……………………………………….... 20 Map 3.6 Zanzibar: Improvement Dairy Goat Population by District…………………… 20 Map 3.7 Sheep Population by District………………………………………………….. 22 Map 3.8 Zanzibar: Pig Population by District…………………………………………... 22 Map 3.9 Zanzibar: chicken Population Destiny………………………………………... 24 Map 3.10 Zanzibar Chicken Population Destiny by District……………………………... 24 Map 3.11 Zanzibar: Indigenous Chicken population by District…………………………. 26 Map 3.12 Zanzibar: :layers Population by district………………………………………... 26 Map 3.13 Zanzibar: Milk Production………………………………………...................... 31 Map 3.15 Zanzibar: Number of households Encountering Lumpy Skin Disease by District……………………………………….......................... 36 Map 3.16 Zanzibar: Number of Households Encountering TTick Borne District……….. 36 Map 3.17 Zanzibar: Number of Households Encountering Heliminthosis by District….. 37 Map 3.18 Zanzibar: Number of Households Encountering Helminthosis by District……. 37 Map 3.19 Zanzibar: Number of Households Encountering Tse tse by District ………….. 38 Map 3.20 Zanzibar: Number of Households Encountering Fowl Typhoid by District…... 38 INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 1 1.0 BACKGROUND INFORMATION Agriculture is an important economic sector of the Zanzibar economy in terms of food production, employment generation, production of raw materials for industries, and generation of foreign exchange earnings. The agricultural sector contributes about 30.8 percent to the GDP (Economic Survey, 2009). Having a diversity of climatic and geographical zones, Zanzibar’s farmers grow a wide variety of food and cash crops as well as fruits, vegetables and spices. In 2009, the percentage share of livestock sub-sector to GDP was 4.5 percent The main types of livestock raised in Zanzibar are cattle, goats, sheep, pigs and chicken. Besides meat production, other products from livestock include hides and skins, milk and eggs. Livestock also contributes to crop and vegetable production by providing draft power for cultivation and organic manure. This report covers the Livestock Sector in Tanzania Zanzibar (Volume VI). Other census reports include; the Technical Report (Volume I), National Crop Report (Volume II), National Livestock Report Volume III, 21 Regional Census Reports for Tanzania Mainland (Volume IV) and Large Scale Farms Report (Volume V) This report is in four main sections: Introduction, Results, Conclusions and Appendices. The definitions relating to all aspects of this report can be found in the questionnaires (Appendix I). 2.0 INTRODUCTION This part of the report provides the technical and operational description of the National Sample Census of Agriculture (NSCA), carried out in the rural areas of Tanzania Mainland and Tanzania Zanzibar for the 2007/08 agricultural year. It also explains the sampling procedures, designing and implementation of the data processing system. 2.1 Rationale for Conducting the National Sample Census of Agriculture The Government of Tanzania has embarked on various plans geared to eradicate extreme poverty by the year 2025 and Tanzania Zanzibar by the year 2020. In order to facilitate intervention and monitoring activities of the Poverty Monitoring Master Plan, the government has planned a series of censuses and surveys to assist in policy formulation, planning and to track on changes in the well-being of the population of Tanzania Mainland and Tanzania Zanzibar. In this Master Plan, a series of Agricultural Censuses and Surveys are planned to be done after every five years. The first one was undertaken in 2002/03 agricultural year, the second for the year 2007/08 and the third one for the year 2012/13 and so on depending on the availability of financial resources. INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 2 Demands for reliable and timely agricultural data have become significantly for monitoring outcomes and progress of the poverty monitoring tools like the Agricultural Sector Development Programme (ASDP) and performance of the respective MDAs (ASLMs). Following the decentralization of the Government’s administration and planning functions, there has been a pressing need for agricultural and rural development data disaggregated at regional and district levels. The availability of district level estimates provides essential baseline information on the state of agriculture that supports decision making by the Local Government Authorities and in the design of District Agricultural Development and Investment Projects (DADIPS). The increase in investment is an essential element in the National Strategy for Growth and Reduction of Poverty. 2.2 Census Objectives The 2007/08 Agricultural Sample Census was designed to meet the data needs of a wide range of users down to district level including policy makers at local, regional and national levels, rural development agencies, funding institutions, researchers, NGOs, farmers’ organizations, and the like. The dataset is numerous in its sample and detailed in its scope and coverage to meet the user demand. The census was carried out in order to:  Identify structural changes in the size of farm household holdings, crop and livestock production, farm inputs and farm implement use. It also seeks to determine if there are any improvements in rural infrastructure and the level of agricultural household living conditions;  Provide benchmark data on productivity, production and agricultural practices in relation to policies and interventions promoted by the Ministry of Agriculture and Natural Resources and other stakeholders  Obtain data that will be used to address specific issues such as: food security, rural poverty, gender, agro-processing, marketing, service delivery, etc. 2.3 Census Coverage The census was conducted for both large and small scale farms. The overall sample for small holders in the 2007/08 Agricultural Sample Census had a total of 317 rural EAs. The data were INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 3 collected from a sample of 4,755 rural agricultural households. Data were also collected from 38 Large scale farms on a complete enumeration basis. 2.3.1 Census Scope The census covered agricultural households in detail as well as many other aspects of rural development. It was conducted using three different questionnaires:  Small scale farm questionnaire;  Community level questionnaire, and  Large scale farm questionnaire. The small scale farm questionnaire was the main census instrument and it included questions related to crop and livestock production and practices, population demographics, access to services, community resources and infrastructure, and issues on poverty and gender. The main topics covered were:  Household demographics and activities of the household members;  Land access/ownership/tenure and use;  Crop and livestock production and productivity,  Access to inputs and farming implements,  Access and use of credit;  Access to infrastructure (roads, district and regional headquarters, markets, advisory services, schools, hospitals);  Crop marketing, storage and agro processing;  Tree farming, agro-forestry, and fish farming;  Access and use of communal resources (grazing land, communal forests, water for humans and livestock, beekeeping);  Investment activities ( irrigation structures, water harvesting, erosion control, fencing);  Off farm income and non agricultural related activities;  Households living conditions (housing, sanitary facilities );  Livelihood constraints; and  Poverty Indicators. The community level questionnaire was designed to collect village level data such as access and use of common resources, community tree plantation and seasonal farm gate price. INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 4 The Large Scale Farm questionnaire was administered to large farms either privately or corporately managed. 2.3.2 Main Activities Undertaken The main focus at all stages of census execution was on data quality which was strongly emphasized all the time. The main activities undertaken include:  Census organization  Tabulation plan preparation  Sample design  Design of census questionnaires and other instruments  Pilot-test  Training of trainers, supervisors and enumerators  Information Education and Communication (IEC) campaign  Data Collection  Field supervision and consistency checks  Data processing: o Scanning o Structure formatting application o Batch validation application o Manual data entry application o Tabulation preparation using SPSS and Excel  Table formatting and charts using Excel, map generation using Arc GIS and Excel  Report preparation using Word and Excel 2.4 Census Methodology 2.4.1 Census Organization The census was conducted by the Office of the Chief Government Statistician, (OCGS), Ministry of Agriculture and Natural Resources and Ministryof Livestock and Fisheries in collaboration with National Bureau of Statistics (NBS). At the national level, the census was headed by Chief Government Statistician in collaboration with the Director General of the National Bureau of Statistics. The Planning Group formed by the Director General of NBS and the Chief Government Statistician of OCGS consisted of staff from the Department of Agricultural Statistics of NBS, INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 5 Department of Economic Statistics of OCGS, Department of Policy and Planning of the Ministry of Agriculture, Food Security and Cooperatives, Department of Policy and Planning of the Ministry of Livestock and Fisheries Development in Tanzania Mainland, the Ministry of Livestock and Fisheries and the Ministry of Agriculture and Natural Resources in Zanzibar. The Planning Group was responsible for all the census operations. The implementation of the census activities at regional level was overseen by the Regional Statistical Officers and Regional Agricultural Officers. At district level, the implementation of the census activities were managed by District Agricultural Development Officers (DADOs) while at National level, there was a national mobile team to supervise the census operations. The Censuses and Surveys Technical Working Group (CSTWG) under MKUKUTA provided support in sourcing financing, approving budget allocations and monitoring progress of the census. A Technical Committee for the census was established with members from key stakeholder organizations and its function was to approve the proposed instruments and procedures developed by the Planning Group. It also approved the tabulations and analytical reports prepared form the census data. 2.4.2 Tabulation Plan Preparation The tabulation plan was developed considering the tabulations from previous 2002/03 census and surveys to allow trend analysis and comparisons as well as the needs of end users. Table 3.1: Census Sample Size 2.4.3 Sample Design The Mainland sample consisted of 317EAs/ villages. These EAs/villages were drawn from the Zanzibar National Master Sample (NMS) developed to serve as a National framework for the conduct of household based surveys in the country. The National Master Sample was developed from the previous 2002 Population and Housing Census. The total of 317 EAs were selected and 4,755 agricultural households were covered (Table 3.1). A two stage sampling was used. The number of villages/Enumeration Areas (EAs) was selected for the first stage with a probability proportional to the number of villages/EAs in each district. In the Description Number Households 4,755 Villages/EAs 317 Districts 9 Regions 5 INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 6 second stage, 15 households were selected from a list of agriculture households in each Village/EA using systematic random sampling. Table 1 gives the sample size of households, villages/EAs and districts. 2.4.4 Questionnaire Design and Other Census Instruments The questionnaires were designed following users demand to ensure that the questions asked were in line with the users data needs. Several features were incorporated into the design of the questionnaires to increase the accuracy of the data:  Where feasible, all variables were extensively coded to reduce post enumeration coding errors;  The definitions for each section were printed on the opposite page so that the enumerator could easily refer to the instructions whilst interviewing the respondent;  The responses to all questions were placed in boxes printed on the questionnaire, with one box per character;  This feature made it possible to use scanning and Intelligent Character Recognition (ICR) technologies for data capture;  Skip patterns were used to reduce unnecessary and incorrect coding of sections which do not apply to the respondent;  Each section was clearly numbered to facilitate the use of skip patterns and provided a reference for data type coding for the programming of CSpro and SPSS. Three other instruments were used;  Village Listing Forms were used for listing the households in the village/EA and from this list, a systematic sample of 15 agricultural households were selected;  A Training Manual was used by the trainers for the cascade/pyramid training of supervisors and enumerators; and  Enumerators Instructions Manual was used as a reference material. INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 7 2.4.5 Field Pilot-Testing The Questionnaire was pilot-tested in both Unguja and Pemba. This was done to test the wording, flow and relevance of the questions and to finalise crop lists, questionnaire coding and manuals. In addition, several data collection methodologies had to be finalized, namely; livestock numbers, mixed cropping, use of percentages in the questionnaire and finalizing skip patterns and documenting consistency checks. 2.4.6 Training of Trainers, Supervisors and Enumerators During the training, cascade/pyramid training techniques were employed to maintain statistical standards. The top level of training was provided to 13 National and regional supervisors. The trainers were members of the Planning Group from the Office of the Chief Government Statistician and Ministries of Agriculture and Natural Resources Livestock and Fishery.. The training concentrated more on questionnaires, listing forms, field level census methodology, and definitions. Emphasis was placed on consistency checking in the field. Tests were given to the supervisors and enumerators and the best 50 percent of the trainees were selected for the enumeration of the smallholder questionnaire and the community level questionnaire. 2.4.7 Information, Education and Communication (IEC) Campaign Radios, televisions, newspapers, leaflets, t-shirts and caps were used to create awareness among the public on the Agriculture Sample Census. This helped in sensitizing the public on field level activities in order to increase the response rate. The t-shirts and caps were given to the field staff and village chairpersons. The village chairpersons assisted in locating the selected households. 2.4.8 Data Collection Data collection activities for the 2007/08 Agricultural Sample Census lasted for three months from June to August, 2009. The interview method was used to collect data during the census. Data collection was monitored by a hierarchical system of supervisors which included the Mobile Response Team, Regional and District Supervisors. The Mobile Response Team, which was headed by the Manager of Agricultural Statistics Department, provided the overall direction to the field operations and responded to queries arising outside the scope of the training exercise. Decisions made on the definitions and procedures were then communicated back to all enumerators via the Regional and District Supervisors.. The enumeration was conducted by staff from the Ministry of Agriculture and Natural Resources and the Ministry of Livestock and Fisheries. Supervision was INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 8 provided by senior officers of the same Ministries and the Office of the Chief Government Statistician. During the household listing exercise, 177 enumerators participated during the listing exercise and enumeration of small holder questionnaire. Additional five percent of the enumerators were kept as reserve in case of drop outs during the enumeration exercise. The enumerators were supervised by District Supervisors. 2.4.9 Field Supervision and Consistency Checks Enumerators were trained on how to probe the respondents until they were satisfied with the response given before they recorded them in the questionnaire. The first check of the questionnaire was carried out by enumerators in the field during enumeration, followed by district, Regional and National Supervisors. Supervisory visits at all levels of supervision focused on checking on the completeness of the questionnaires and consistency. Inconsistencies encountered were corrected, and where necessary, call backs to the respondents were made by the enumerators to obtain the correct information. Further quality control checks were made by supervisors in each district. 2.4.10 Data Processing and Analysis Data processing involved the following processes:  Data entry;  Data structure formatting;  Batch validation; and  Tabulation. 2.4.11 Data Entry Scanning and ICR data capture technology was used. This did not only increase the speed of data entry but it also increased the accuracy of the data due to the reduction of keystroke errors. Interactive validation routines were incorporated into the ICR software to trap errors during the verification process. Prior to scanning, all questionnaires underwent a manual cleaning exercise by checking that the questionnaire had a full set of pages, correct identification and good hand-writing. A score was given to each questionnaire based on the legibility and the completeness of the enumeration. This score was used to assess the quality of enumeration and supervision. CSPro was used for data entry of questionnaires that were rejected by ICR extraction application. INTRODUCTION Tanzania Agriculture Sample Census - 2007/08 9 2.4.12 Batch Validation A batch validation program was developed in CSPro in order to identify inconsistencies within a questionnaire. This is in addition to the interactive validation during the ICR extraction process. The procedures varied from simple range checking within each variable to more complexes checking between variables. It took 6 months to screen, edit and validate the data from the smallholder questionnaire. After the long process of data cleaning, the tabulations were prepared based on the pre-designed tabulation plan. 2.4.13 Tabulations Statistical Package for Social Sciences (SPSS) was used to produce the Census tabulations and Microsoft Excel was used to organize the tables and compute additional indicators. Excel was also used to produce charts while Arc GIS was used for producing the maps. 2.4.14 Analysis and Report Preparation The report writing was outsourced to Sokoine University of Agriculture, the analysis in the reports focused on district comparisons, time series and National production estimates. Microsoft Excel was used to produce charts; Arc GIS and Excel were used to generate maps, whereas Microsoft Word was used in the compilation and writing the report. 2.4.15 Data Quality Control A great deal of emphasis was placed on data quality throughout the whole exercise from planning, questionnaire design, training, supervision, data entry, validation and cleaning/editing. As a result of this process, it is believed that the census is highly accurate and representative of what was experienced at field level during the Census Year. With very few exceptions, the variables in the questionnaires were within the norms for Tanzania and they followed the expected time series trends when compared to historical data. 2.5 Funding Arrangements The 2007/08 Agricultural Sample Census was supported mainly by the Department for International Development (DFID) and the Japan International Cooperation Agency (JICA) who financed most of the operational activities. Other funds for operational activities were from the Government of Tanzania. In addition, technical assistance was provided by the Food and Agricultural Organization (FAO). RESULTS Tanzania Agriculture Sample Census - 2007/08 10 Chart 3.2 Number of Households Keeping Livestock by Type Pigs, 153 Donkeys, 296 Ducks, 3321 Sheep,210 Chicken, 80,069 Rabbits,198 Cattle, 39420 Goats, 13,107 Turkey, 229 Goats Cattle Sheep Pigs Donkeys Rabbits Ducks Turkey Chicken Chart 3.1 Total Number of Livestock by Type Cattle, 155,624, 68.1% Sheep, 574, 0.3% Donkeys, 353 0.2% Goats, 68972 30.2% Pigs, 3015 1.3% Goats Cattle Sheep Pigs Donkeys 3.0 LIVESTOCK AND POULTRY RESULTS 3.1 Livestock Population and Growth Livestock sector including poultry plays a significant role in the economy of agricultural households in Tanzania Zanzibar. Livestock generate considerable amount of cash income and determine the household economic and social status in many communities. An estimated 45,684 households (About 35 % of agricultural households) kept livestock (excluding poultry). The main types and number of livestock and poultry covered in the 2007/08 National Sample Census of Agriculture are cattle, goats, sheep, pigs, chicken, ducks, turkeys, rabbits, donkeys. This section analyzes the results in relation to the population, growth rates, husbandry and the provision of services at district levels. Also, it includes data on population and growth rate trends on livestock in comparison with the previous Agricultural Sample Census for the period between 1995 and 2008. In the surveyed households, cattle were the most dominant specie amounting to 155,624 (68%) followed by goats 68,972 (30%), pigs 3,015 (2%), sheep 574 (0.3%) and donkeys 353 (0.2%) (Chart 3.1). Other livestock species were chicken 1,078,962, Ducks 34,279, rabbits 1,262, turkeys 881 and dogs 4,214. Out of the total chicken 932,469 were indigenous, 130,034 were layers and 16,459 were broilers. The number of households keeping different types of livestock were as follows: those kept chicken were 80,069 (59%), cattle were 39,420 (29%), goats were 13,107 (10%) and ducks were RESULTS Tanzania Agriculture Sample Census - 2007/08 11 3,321(2%). However, households which reared sheep, pigs, donkeys, turkeys and rabbits were very few (Chart 3.2). Table 3.2 summarizes production data for different types of livestock. Ducks, Turkeys, Rabbits and Donkeys are of relative minor importance. On average, households kept about four cattle, five goats, three sheep, thirteen chicken and 20 pigs. Very few households kept ducks, turkeys, rabbits, and donkeys. For example, an average of one donkey was kept per household. Table 3.2: Number of Livestock by Type Expressing livestock number in terms of livestock units (LSU), the results show that, there was an equivalent of 170,715 livestock units in total representing 228,538 major livestock of different species (cattle, goats, sheep, pigs and donkeys). Cattle livestock units were 155,624, goats 13,794, sheep 115, pigs 1,005 and donkeys 176 units. The LSU is used to estimate total quantity of livestock based on cow having a LSU of 1, a goat or sheep 1/5 LSU, a pig 1/3 LSU and a donkey 1/2 LSU. 3.1.1 Cattle Population The total number of cattle raised in by the smallholders was 155,624 heads out of which, the indigenous type represented 95.5% of the total cattle population. Table 3.3: Number of Households and Number of Cattle by Herd Size On average, the herd size of cattle per holding in the smallholder sector was 4 heads (Table 3.1). Of the total 39,420 cattle keeping households, 95.4 percent reared between 1 and 10 heads. Three percent of the households reared between 11 and 15 cattle while there were few households with more Livestock Type No. of Household No. of Livestock No. per Household Cattle 39,420 155,624 4 Goats 13,107 68,972 5 Sheep 210 574 3 Pigs 153 3,015 20 Donkeys 296 353 1 Ducks 3,321 324,279 10 Turkeys 229 881 4 Chicken 80,069 1,078,962 13 Rabbits 198 1,262 6 Herd size Cattle Rearing Households % Herd of Cattle % Average Per Houseold 1 - 5 31,627 80.2 83,610 53.7 2.64 6 - 10 6,001 15.2 43,716 28.1 7.28 11 - 15 1,232 3.1 15,662 10.1 12.71 16 - 20 331 0.8 5,797 3.7 17.52 21 - 30 148 0.4 3,704 2.4 25.09 31 - 40 51 0.1 1,736 1.1 34.00 41 - 50 30 0.1 1,398 0.9 46.00 Total 39,420 100 155,624 100 3.95 RESULTS Tanzania Agriculture Sample Census - 2007/08 12 Chart 3.3 Cattle Population and Average Head per Household by District 0 5,000 10,000 15,000 20,000 25,000 30,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani 0 1 2 3 4 5 6 Number of Cattle Number of Cattle per Households Chart 3.4 Cattle Population Trend 155,624 162,643 111,693 148,744 154,381 108,346 0 25,000 50,000 75,000 100,000 125,000 150,000 175,000 1993 2003 2008 Year Number of Cattle Total Cattle Population Indigenous Cattle than 15 cattle per household. Their range is between 18 and 46 heads per household (Table 3.3). However, smallholders with fewer animals (1 to 5) raised more than half of the cattle population. Central district had the largest cattle population (27,662) followed by Micheweni (23,419), Wete (21,934) and West (21,132) (Chart 3.3, Maps 3.1 and 3.2). Other districts with relatively large number of cattle are Mkoani (16,976), Chakechake (15,982), North ‘B’ (15,677). Although North ‘A’ and South districts had fewer numbers of cattle, the number of cattle per household was comparable to that of Wete district which had a large number of cattle population. South district had the least number of cattle compared to other districts (Chart 3.3). Highest cattle population density was in West and Wete (89 cattle per square kilometre), followed by Mkoani (22), Chakechake (70), North B (64). The lowest density was in South (12 cattle per Sq. Km) (Map 3.2). Cattle population (both indigenous and exotic or their crosses) has increased by approximately 27 percent from about 111,693 heads in 1993 to 155,624 heads in 2008. However, in the period between 2003 and 2008, the total cattle population of the smallholder has decreased by 4.5% from 162,643 to 155,624 heads giving an annual negative growth rate of about 0.9 percent per annum over the five year period. The indigenous cattle population also has decreased from 154,381 to 148,744 heads representing a total decrease of 3.7% and a negative annual growth rate of 0.75% (Chart 3.4). RESULTS Tanzania Agriculture Sample Census - 2007/08 13 Chart 3.5 Percentage of Households Rearing Indigenous Cattle by District 0 1,500 3,000 4,500 6,000 7,500 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number of households 0 5 10 15 20 Percentage Number of households Percentage Chart 3.6: Percentage of Indigenous Cattle by District 93.3 98.4 92.6 91.5 95.4 99.0 94.0 99.7 99.7 86 88 90 92 94 96 98 100 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Percentage Percentage of Indigenous Cattle Chart 3.7 Percentage of Households Rearing Improved Dairy Cattle by District 3.9 4.2 30.1 0.7 27.2 20.1 2.4 10.2 1.1 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 North 'A' North 'B' Kati South West Wete Micheweni Chakechake Mkoani District Percentage Indigenous Cattle Population The cattle population is mainly dominated by the indigenous type 95.6%, while the improved dairy cattle contributed only 4.5 percent and no improved cattle for beef. The census results show a decrease in the number of indigenous cattle from 108,346 in 1993 to 148,744 heads in 2008 representing a 3.7 percent decrease (Chart 3.4). Districts with more households rearing indigenous cattle include; Micheweni district reared (18.3%) followed by Wete (15.4%), Mkoani (14.8%) and Central district (12.1%). The same districts had more indigenous cattle than other districts (Charts 3.5 and Charts 3.6). Nevertheless, the following districts had moderate percentages of households with livestock: Chakechake (11.9%), West (11.6%) and North ‘B’ (8.2%). However, North ‘A’ (4.6%) and South (3.2%) districts had least number of households rearing indigenous cattle than other districts. In total, the indigenous cattle accounted for more than 90% of the entire cattle population in each district (Chart 3.6). Improved Cattle Population All of the improved cattle are of dairy type (100%) reared by 2,422 households and there were no improved cattle for beef. The Central district (Kati) had 730 households rearing dairy cattle representing 30.1 RESULTS Tanzania Agriculture Sample Census - 2007/08 14 Chart 3.8 Percentage of Improved Dairy Cattle by District 7.8 3.7 29.6 0.2 14.5 3.4 14.0 0.8 26.0 0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 North 'A' North 'B' Kati South West Wete Micheweni Chakechake Mkoani District Percentage 3337 7908 6880 0 1,500 3,000 4,500 6,000 7,500 9,000 Number of Cattle 1992/93 2002/03 2007/08 Year Chart 3.9 Improved Dairy Cattle Population Trend 45,115 52,324 68,972 0 15,000 30,000 45,000 60,000 75,000 Number of Goats 1993 2003 2008 Year Chart 3.10 Goats Population Trend percent of the total households followed by West with 659 (27.2%), Wete with 487 (20.1%) and Chakechake with 248 (10.2%) households. North-B, North ‘A’, Micheweni, Mkoani and South districts had very few (less than 5%) households engaged in the rearing of improved dairy cattle (Chart 3.7). In terms of numbers, the Central district had the highest population of dairy cattle amounting to 2,037 (29.6%), followed by West 1,790 (26%), Wete 999 (14.5%) and Chakechake 961 (14%). The contribution of North ‘A’ to the total number of improved dairy cattle was only 7.8%. North-B, Micheweni, Mkoani and South had the least number of improved dairy cattle (Chart 3.8, Map 3.3). Over the past 15 years, the number of dairy cattle (pure or their crosses) has increased by 137% between 1993 and 2008 despite that the number of dairy cattle has declined by 13% between 2003 and 2008. In the overall, there was a two fold increase from about 3,337 heads in 1993 to 6,880 heads in 2008 representing an annual growth rate of 7% (Chart 3.9). 3.1.2 Goat Population The total number of goats raised by smallholders was 68,972. Goat population increased from 45,115 in 1993 to 68,972 in 2008, representing an increase of about 53 percent and an annual RESULTS Tanzania Agriculture Sample Census - 2007/08 15 Chart 3.12: Number and Percent of Goats by District 12 9 24 7 17 3 10 7 11 0 2,500 5,000 7,500 10,000 12,500 15,000 17,500 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number of Goats 0 5 10 15 20 25 Percentage Number of Goats % of Goats Population Chart 3.11: Number and Percentage of Households Rearing Goats 11 9 17 6 13 14 6 10 14 0 500 1,000 1,500 2,000 2,500 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number of Households 0 5 10 15 20 Percentage No of Households % of Households growth rate of 3.5% over the 15 years period (Chart 3.10). Between 1993 and 2003, the percentage increase was 16% while the growth has doubled to 32% between 2003 and 2008. The average number of goats per household was 5 goats. Most of the households (90.5%) raised between 1 to 9 goats representing 60.7% of the total goat population. The remaining households (9.5%) raised 39.4% of the goats (Table 3.4). Table 3.4: Number of Households Raising Goats by Herd Size With regard to households raising goats a total of 13,107 households were reported to managed goats. Central district had more households (17%) followed by Mkoani and Micheweni with 14 percent each, West (13%), North ‘A’ (11%), and Chakechake(10%). Other districts with lower percentages are North ‘B’(9%), South (6%) and Wete (6%) (Chart3.11). The leading districts with more goats include Central with 16,415 (24%), West with 12,026 (17%) and North ‘A’ district with 8,222 (12%) ((Table 3.4 & Map 3.4). Herd Size Households Goat Number Number Percent Number Percent Per Household 1 - 4 8,372 63.9 20,734 63.9 2 5 - 9 3,486 26.6 21,131 26.6 6 10 - 14 826 6.3 9,369 6.3 11 15 - 19 163 1.2 2,536 1.2 16 20 - 24 58 0.4 1,289 0.4 22 40+ 202 1.5 13,914 1.58 69 Total 13,107 100 68,972 100 5 RESULTS Tanzania Agriculture Sample Census - 2007/08 16 RESULTS Tanzania Agriculture Sample Census - 2007/08 17 RESULTS Tanzania Agriculture Sample Census - 2007/08 18 3.13: Number of Goats by Type and District 8,694 7,801 6,775 6,269 4,627 4,500 1,973 10,079 6,286 0 2,000 4,000 6,000 8,000 10,000 12,000 West Central Mkoani Micheweni North 'B' North 'A' Chakechake South Wete District Number of Goats Indigenous Improved Dairy Chart 3.14: Percentage of Sheep Population by District North 'A', 5% North 'B', 9% Central, 21% West, 49% Micheweni, 15% North 'A' North 'B' Central West Micheweni The districts of South, Chakechake and Wete had the least numbers of goats. Out of 13,107 agricultural households rearing goats, 12,817 (97.8%) households reared indigenous goats, 444 (3.4%) households kept improved goats and only 32 (0.24%) households kept improved goats for meat. Highest goat densities were found in West, South and Central (51 goats per Sq. km). Others wer North (34), Mkoani (34) and Micheweni (25) Wete district had the lowest goat concentration of only 9 goats per Sq. km (Map 3.5). The number of indigenous goats was 57,004 (82.6%), improved dairy goats 11,905 (17.3%) and 63 (0.1%) improved goats for meat. In total the number of improved goats (Improved for meat and Improved dairy) was 11,968 or 17.4% of the total goat population. Most of the improved goats were in the Central district (7,721), West district (1,947) and North ‘A’ district (1,890). However, the districts of South, North-B, Micheweni and Mkoani had no improved goats (Chart 3.13, Map 3.6) RESULTS Tanzania Agriculture Sample Census - 2007/08 19 640 300 574 0 200 400 600 800 Number of Sheep 1992/93 2002/03 2007/08 Year Chart 3.15: Sheep Population Trend Number of Sheep 3.1.3 Sheep Population Sheep keeping is insignificant in Zanzibar with only 210 households engaged in sheep rearing. All the reared sheep were of indigenous type. Most of the sheep were found in five districts namely: West (49%), Central (21%), Micheweni (15%), North ‘B’(9%) and North ‘A’ (6%) (Chart 3.14). The average number of sheep per household rearing sheep was 3. Unlike goat population which increased between 1993 and 2003, sheep population declined by 53 percent in the same period. However, the trend was reversed between 2003 and 2008, whereby the number of sheep increased from 300 to 574 representing a 91 percent increase. On the overall, the annual growth rate over the 15 years period was only 0.6 percent (Chart 3.15). RESULTS Tanzania Agriculture Sample Census - 2007/08 20 RESULTS Tanzania Agriculture Sample Census - 2007/08 21 1992/93 2002/03 2007/08 66 535 3,015 0 1,000 2,000 3,000 4,000 Number of Pigs Year Chart 3.16: Pigs Population Trends Number of Pigs Chart 3.17 Number of Households Rearing Chicken by Diatrict 0 2,500 5,000 7,500 10,000 12,500 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number of Households 3.1.4 Pig Population The number of pigs as by 1st October, 2008 was estimated to be 3,015 heads. These were kept by 153 households, representing an average of 20 pigs per household. Almost all the pigs were kept in the South Unguja and Urban West regions (62.5%) in the central district and (37.5%) in the West district. About 40 percent of the households kept between 5 and 9 pigs, 20 percent kept between 15 and 19 pigs and the remaining 40percent of the households raised between 30 and 39 pigs. Moreover, most of the pigs (67.7%) were raised by 40 percent of the households in the category of 30-39 pigs. In this cluster, the average number of pigs per household was 33 heads (Table 3.5). In total, the average number of pigs per household was 20. Also, over the fifteen years period, the pig population has increased dramatically from 66 heads in 1992/1993 to 3,015 heads in 2007/2008. This represents an increase of 4,468 percent with an average annual growth rate of 298 percent (Chart 3.16). Table 3.5: Number of Households Raising Pigs by Herd Size 3.1.5 Chicken Population Many households in Zanzibar keep chicken especially the indigenous ones or their crosses with either layer or broiler types (hereafter referred to as local). The census results show that 80,069 households equivalent to 60.6 percent of all the agricultural households were engaged in poultry keeping. These households kept 1,078,962 chickens of which 86.4 percent were indigenous, 12.1 percent were layers and 1.5 percent were broilers. More households in Wete, West, Micheweni and Mkoani districts kept chicken compared to other districts (Chart. 3.17). Herd Size Pig Rearing Households Herd of Pigs Average per HH Number % Number % 5 - 9 61 40 395 13.1 6.5 15 - 19 30 20 578 19.2 19.0 30 - 39 62 40 2,042 67.7 33.0 Total 153 100 3,015 100 19.7 RESULTS Tanzania Agriculture Sample Census - 2007/08 22 RESULTS Tanzania Agriculture Sample Census - 2007/08 23 Chart 3.18 Chicken Population by District 0.0 5.0 10.0 15.0 20.0 25.0 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Percentage 0 5 10 15 20 25 Head per Household Percentage Head per household 1992/93 2002/03 2007/08 790,089 1,063,791 1,078,962 - 250,000 500,000 750,000 1,000,000 1,250,000 Number Year Chart 3.19: Number of Chicken Population Trend In terms of number of chicken, West district kept 21 percent of the total chicken population. Other districts with relatively high population of chicken were Wete, Mkoani and North ‘B’(Chart. 3.18 and Map 3.10) whereas South and North ‘A’ districts had the least number of chicken. About 75 percent of the total chicken population was kept by 96.9 percent of the households whereby the flock size was in the range of 1 to 49. These were mainly indigenous/ local types (Table 3.6). The remaining 25percent of the chicken were kept by 2,438 (3%) of the total chicken raising households. The average number of chicken per household was 13. However, the number of chicken has increased from 790,089 to 1,078,962 (an increase of 36.6 percent) over the 15 years period (1993 to 2008) (Chart 3.19). RESULTS Tanzania Agriculture Sample Census - 2007/08 24 RESULTS Tanzania Agriculture Sample Census - 2007/08 25 1992/93 2002/03 2007/08 712,473 944,371 932,469 - 250,000 500,000 750,000 1,000,000 Number Year Chart 3.20: Indigenous Chicken Population Trend Chart 3.21: Number of Indigineous Chicken 132,905 101,893 97,851 48,025 76,455 99,918 100,476 135,501 139,445 0 25,000 50,000 75,000 100,000 125,000 150,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number Number of Indigineous Chicken Table 3.6: Households Raising Chicken by Flock Size Indigenous Chicken Population There were 932,469 or 86% of the total indigenous chicken. The number has increased from 712,473 in 1992/1993 to 932,469 in 2007/08, an overall increase of 31% and a 2% average growth per annum over the 15 years period (1993 to 2008) (Chart 3.20). About 98 percent of the local chicken keeping households kept less than 50 birds per household and the households had a chicken population of 76,731 or 85 percent of the total local chicken . Only about 2 percent of the households kept more than 50 chicken per household which represented 15 percent of the total chicken population. However, there was no household with 500 or more local chicken (Table. 3.7). Table 3.7: Households Raising Local Chicken by Flock Size Most of the indigenous chicken were kept in West, Wete and Mkoani districts. Other districts with about 10% of the populations include North- B, Central, Micheweni and Chakechake while. South district had the least number of indigenous chickens (Chart 3.21 and Map 3.11). Improved Chicken Population The number of improved chicken in the smallholder sector is relatively small. The survey results show that there were about 146,493 chicken representing 13.6 percent of the total chicken population. About 12.1 percent of the improved chickens were layers and 1.5 percent was broilers. Flock Size Number of Households % Number of Chicken % 1-49 77,631 96.9 804,726 75 50-99 1451 1.81 86,245 8 100-299 842 1.05 124,647 12 300-499 114 0.14 38,225 4 700+ 31 0.04 25,120 2 Total 80,069 100.00 1,078,962 100 Flock Size Indigineous chicken Number of Households % Number of Indigenous Chicken % 1-49 76,731 98 795,432 85 50-99 1,306 2 76,320 8 100-299 359 0 50,537 5 300-499 25 0 10,180 1 700+ 0 0 0 0 Total 78,422 100 932,469 100 RESULTS Tanzania Agriculture Sample Census - 2007/08 26 RESULTS Tanzania Agriculture Sample Census - 2007/08 27 35,712 41,904 130,034 16,459 - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 Year 1992/93 2007/08 Number Chart 3.22: Improved Chicken Population Trend Layers Broilers Chart 3.23: Percentage and Number of Improved Checken by District - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 West North 'B' Wete North 'A' Chakechake South Central Micheweni Mkoani District Number 0.0 15.0 30.0 45.0 60.0 75.0 Percentage Number Percentage Chart 3.24: Layer Population by District - 10,000 20,000 30,000 40,000 50,000 60,000 70,000 80,000 90,000 100,000 West North 'B' Wete North 'A' Chakechake South Central Micheweni Mkoani District Number 0 15 30 45 60 75 Percentage Number of Layers Percentage The number of layers increased from 35,712 in 1992/1993 to 130,034 in 2007/2008 an increase of 264 percent, while the number of broilers decreased from 41,904 to 16,459 a decrease of 60.7 percent (Chart 3.22). Most of the layers and broilers were kept mainly in the West district followed by North ‘B’district, Wete and North ‘A’ (Chart 3.23, Map 3.12). The 52 percent of the household that kept layers raised between 1 and 49 chicken; however, this cluster raised only 5.6 percent of the total layer population. About 29 percent of the households kept between 100 and 299 birds representing 45.8 percent of the total number of layers. Almost two thirds of the households kept broilers, and the flock size ranged between 1 and 49 birds. The remaining one third kept 88 percent of the broiler population (Table 3. 8). The percentage of layers in West and North B districts were relatively higher at about 62percent and 12percent respectively. The remaining districts had a total contribution of 26percent of the improved layer population (Chart 3.24). North ‘B’and West districts had the highest concentration of broilers with contributions of 30.9 percent and 57.2 percent respectively. Districts of South, Central, Wete, Chakechake and Mkoani had no broilers or their percentage contributions were negligible. RESULTS Tanzania Agriculture Sample Census - 2007/08 28 Table 3.8: Improved Chicken Population by Flock Size Flock Size Layers Broilers Number of Households Number of Layers % Number of Chicken Per Household Number of Households Number of Broilers % Number of Chicken Per Household 1-49 721 7,345 5.6 10 179 1,949 12 11 50-99 145 9,925 7.6 68 0 0 0 0 100-299 394 59,600 45.8 151 88 14,510 88 164 300-499 88 28,045 21.6 318 0 0 0 0 700+ 31 25,120 19.3 800 0 0 0 0 3.1.6 Other Livestock Other livestock includes ducks, guinea pigs, turkeys, rabbits and donkeys. They are less important to the overall contribution to household food security and are kept by fewer households. Proportionally, there were more ducks compared to other livestock types and donkeys were the least in the population. Donkeys are mainly used as pack animals. Most of the ducks and rabbits were found in West district with contributions 47 percent for ducks and 57percent for rabbits, while guinea pigs were more in North B and Central districts and donkeys were concentrated more in Chakechake district (Table 3.9). Table 3.9: Population of Other Livestock by District District Ducks % Guinea pigs % Turkeys % Rabbits % Donkeys % North ‘A’ 6,332 18 0 0 0 0 0 0 63 18 North-B 4,556 13 331 40 305 35 0 0 0 0 Central 2,097 6 213 26 122 14 0 0 30 9 South 1,803 5 81 10 244 28 97 8 0 0 West 16,077 47 0 0 157 18 722 57 0 0 Wete 1,589 5 0. 0 0 0 256 20 51 15 Micheweni 555 2 175 21 0 0 0 0 0 0 Chakechake 922 3 23 3 0 0 186 15 155 44 Mkoani 348 1 0 0 54 6 0 0 54 15 Total 34,279 100 823 100 881 100 1,262 100 353 100 However, more households kept ducks than any other species (Table 3.10). The average number of ducks per household was 10 while the average number of turkeys, rabbits and donkeys per household were 4, 6 and 1 respectively. RESULTS Tanzania Agriculture Sample Census - 2007/08 29 Table 3.10: Number of Households and Livestock by Type Type of Livestock Number of Household Number of livestock Average number per HH Ducks 3,321 34,279 10 Turkeys 229 881 4 Rabbits 198 1,262 6 Donkey 296 353 1 Others 1839 5037 3 3.2 Livestock products -Milk Production Most of the milk is produced from cows. In Zanzibar there was a total of 44,718 cows milked during the wet season (6.5% improved type; 93.5% indigenous type) and 36,639 cows during the dry season (7.6 % improved type; 92.4% indigenous types). The total milk production during the wet season was 111,616 lt and dropped to 84,385 lt during the dry season. The average milk production was 2.5 liters during the wet season and 2.3 lt during the dry season. West district ranked highest in total production (both during the wet and dry season (23 and 27% of total production respectively). It was followed by Central (19%), Wete (13%) and North B (14%). The four districts produced about 69% of total milk during the wet season. Other district produced moderate amount of milk and the least were North A and Mkoani (Table 3.11, Map 3.13). Table 3. 11: Total Milk Production and Percentage by District The average milk production per cow per day was 7 liters and 2 litres for improved dairy type and local type during both the wet and dry season respectively. However, highest production during the two seasons was recorded in North A and West for improved types with average of 12 and 10 litres respectively. Milk production from indigenous cows did not differ much between district and ranged from 2 to 3 litres per day during the wet season. During the dry season the range in milk production per cows was between 1 and 2 litres per day (Table 3.11). The disparity between district for improved type could be linked to type of improved animals (exotic blood levels), and management. The price of milk varied between districts, seasons and type of animals. During the wet season the average price of milk from improved cattle was slightly higher that that from indigenous cattle, being 507 Tsh and 479 Tsh respectively. District Wet season (Lt) Percet Dry Season (Lt) Percent North 'A' 9623 9 7225 9 North 'B' 15298 14 11779 14 Central 21548 19 11894 14 South 1610 1 868 1 West 25651 23 22832 27 Wete 13970 13 11623 14 Micheweni 9482 8 8168 10 Chakechake 9626 9 8139 10 Mkoani 8213 7 4962 6 Total 111616 84385 RESULTS Tanzania Agriculture Sample Census - 2007/08 30 Table 3.12: Average Milk Production per Cow per day, by Category of Cow, Season and District The same pattern was observed during the dry season, whereby milk from improved cattle increased to 522 Tsh (2.9% increase), while that of indigenous cattle was 495 Tsh (an increase of 3.3%) (Table 3.12). During the wet season, highest price per litter was recorded in North A (570 Tsh) followed by Wete (507 Tsh), West (502 tsh) and Mkoani (501 Tsh). For the remaining districts the prices were near equal ranging from 449 Tsh to 491 Tsh in South district. Near similar pattern was displayed during the dry season, North A leading in the price charged per liter. For both season lowest price were found in central district whith price being 450 Tsh and 461 Tsh respectively. Table 3.13: Average Cattle Milk price (Tshs/litre) per season by category of cow and District Milk production from goats was insignificant. A total of 1018 litres were estimated to be produced during 2007/08 agricultural year from 880 goats each producing on average about 1.2 litres per day. The price of goat milk was however, higher than that of cow’s milk, being 903 Tsh on average (Table 3.13). District Wet Season Dry Season Improved Breed Indigenous Total Improved Breed Indigenous Total Mean (ltr) Mean (lts) Mean (lts) Mean (lts) Mean (lts) Mean (lts) North 'A' 12 3 3 12 2 2 North 'B' 6 3 3 6 2 3 Central 6 2 3 5 2 2 South . 2 2 2 1 1 West 10 3 4 10 3 4 Wete 6 2 2 6 2 2 Micheweni 4 2 2 4 2 2 Chakechake 6 2 2 7 2 2 Mkoani 0 2 2 0 2 2 Total 7 2 2.5 7 2 2.3 District Wet Season Dry Season Improved Breed Indigenous Total Improved Breed Indigenous Total Mean Mean Mean Mean Mean Mean North 'A' 900 553 570 800 565 572 North 'B' 450 449 449 450 468 467 Central 435 453 450 445 465 461 South . 491 491 750 492 512 West 541 494 502 583 508 518 Wete 491 508 507 509 496 497 Micheweni 575 455 459 633 484 488 Chakechake 557 445 456 543 482 488 Mkoani 60 506 501 0 533 533 Total 507 479 481 522 495 497 RESULTS Tanzania Agriculture Sample Census - 2007/08 31 RESULTS Tanzania Agriculture Sample Census - 2007/08 32 3.3 Contribution of Livestock to Crop production In addition to provision of milk and draught power, livestock has important contribution to crop production in terms of manure provision. In Zanzibar, a total of 6,806 households (7.7% of all households planting during Long rain) use organic fertilizers (mainly manure). Organic fertilizer was used on 2,926 ha representing 7.8 percent of the total planted area during long rain season (MASIKA) (Table 3.14). Districts with higher proportion of area planted with organic manure were Central (21.7%), South (16.5%), North B (14.1%0 and West (13.7%). Uses of organic manure in other districts were less than 10 percent of planted area during long rain, the least being Mkoani (3.2) and Chakechake (1.9%) (Table 3.14) . The extent of fertilizer use does not match with the number of livestock kept. For example, Michweni and Wete ranked second and third respectively in terms of number of cattle, but were among the lowest user of organic fertilizers. Table 3.14 Animal Contribution to Crops: Number of Households and Planted Area by Organic Fertilizer use and District - Long Rainy Season Districts Organic Fertilizer Use % of Planted area using Organic Fertilizer Number of Households using Organic Fertilizer Planted Area Applied with Organic Fertilizer Number of Households NOT using Organic Fertilizer Planted Area NOT Applied with Organic Fertilizer Total Number of Households Planting in MASIKA Total Planted Area in MASIKA North 'A' 1,134 437 11,845 5,435 12,979 5,872 7.4 North 'B' 1,120 487 6,261 2,961 7,380 3,448 14.1 Central 1,246 721 5,259 2,600 6,505 3,321 21.7 South 227 30 910 150 1,137 179 16.5 West 1,162 388 6,782 2,436 7,944 2,825 13.7 Wete 743 283 13,248 5,804 13,991 6,087 4.6 Micheweni 701 304 12,848 4,959 13,549 5,263 5.8 Chakechake 178 96 10,991 5,055 11,169 5,152 1.9 Mkoani 295 179 13,415 5,327 13,709 5,506 3.2 Total 6,806 2,926 81,558 34,727 88,364 37,653 7.8 3.4 Livestock Diseases and control 3.4.1 Common Livestock Diseases The most common diseases infecting ruminant livestock include Tick Borne Diseases (T.B.D), Foot and Mouth Diseases and Lumpy Skin Disease (LSD), while in poultry, the Newcastle disease and Fowl Typhoid were the most challenging. Newcastle disease infected 53,530 households and these presenting 59 percent of the total livestock rearing households (91,380). RESULTS Tanzania Agriculture Sample Census - 2007/08 33 Chart 3.25 Percentage of Households Affected by Various Livestock Diseases 7,834, 9% 4,513, 5% 53,530, 59% 30,121, 33% 20,036, 22% Tick Newcastle Fowl Typhoid Foot and Mouth Lympyskin Chart 3.26: Number and Percentage of Livestock rearing Householhs Encountering Lumpy Skin Disease by District 0 500 1,000 1,500 2,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number 0 5 10 15 20 25 Percentage Number Percentage The number of households reporting Fowl Typhoid, infection was 20,036 representing 22 percent of the total livestock rearing households. The number of households rearing livestock reported to be infected by Tick Borne Disease were 30,121 (33%), Lumpy Skin disease 7,834 (9%), and Foot and Mouth Disease (FMD) 4,513 (5%) of the total households rearing livestock (Chart 3.25). Lumpy Skin Disease (LSD) A total of 7,834 (9%) cattle rearing households have reported to have been affected by the Lumpy Skin Desease. The highly infected districts with the disease were: Central with 1,763 (23%) households, this folllowed by West with 1,664 (21%) households, Wete district accounted 1,127 (14%) households rearing livestock. Fewer incidences of the disease were in North ‘A’ district where only 4 percent of the households reported to have been affected by the disease. About nine and seven percent of the households in Micheweni and Chakechake districts reported the incidences respectively. South and Mkoani districts had three and nine percents of cases of the cattle keeping households. In Wete district, 1,127 households rearing livestock equivalent to 14 percent of the cattle keeping households were affected with the LSD (Chart 3.26, Map 3.15). Tick Borne Disease Incidences of Tick Borne Disease (TBD) were highest reported in Central district with 4,377 households or 48 percent of the households rearing livestock within the district. Chakechake district had 4,201 (39%) cases followed by Micheweni district with 4,585(36%) reported cases within the district. Other districts with moderate cases were Mkoani district with 3,981(34%) cases, RESULTS Tanzania Agriculture Sample Census - 2007/08 34 Wete district with 4,254 (33%) cases and North ‘B’district with 2,672 (33%) cases (Chart 3.27, Map 3.16). Worm Problems The distribution of households reporting worm problems (Helminthosis) is presented in Map 3.17. There were more household encountering worm problems in West, followed by Central district. Other districts with moderate intensity were North A, North B, Micheweni and Wete. Mkoani reported fewer incidences of worms. Tse tse problems Although there were programmes to eradicate Tsetse in Zanzibar, the 2007/08 agricultural census still indicate that the problem still exist. Map 3.18 shows that In Pemba Island there were more tse tse cases than in Unguja island. Mkoani district had higher number of households reporting Tsetse problems (714), It was followed by Micheweni (555 hh), Chakechake ( 403 hh). In Unguja more incidences were reported in West (471 hh), North A (410 hh) and Central (304 hh) (Map 3.18). Foot and Mouth Disease Foot and Mouth Disease was one of the serious diseases which was reported to infect 4,513 (10%) of the total households rearing livestocks. The highly infected districts include Central with 1,216(14%) households and West district with 1,507 (12%) households. South and North ‘B’districts had few households reporting to encounted Foot and Mouth Disease with only 6 percent and 7 percent respectively. Incidences of FMD were the least in Wete and Chakechake districts, each with one percent followed by North ‘A’ (3%). In Micheweni and Mkoani districts only 2 percent of the cattle rearing households reported the FMD cases in each of the districts (Chart 3.28, Map 2.22). RESULTS Tanzania Agriculture Sample Census - 2007/08 35 Chart 3.28: Number and Percentage of Livestock Rearing Householhs Encountering Foot and Mouth Disease by District 0 500 1,000 1,500 2,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number 0 2 4 6 8 10 12 14 16 Percentage Number Percentage Chart 3.27: Number and Percentage of Livestock Rearing Householhs Encountering Tick Problem by District 0 1,000 2,000 3,000 4,000 5,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number 0 10 20 30 40 50 60 Percentage Number Percentage Chart 3.29: Number and Percentage of Livestock Rearing Householhs Encountering Newcastle Problem by District 0 1,500 3,000 4,500 6,000 7,500 9,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number 0 25 50 75 100 Percentage Number Percentage Chart 3.30: Number and Percentage of Livestock Rearing Householhs Encountering Fowl Typhoid by District 0 1,000 2,000 3,000 4,000 5,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number 0 10 20 30 40 Percentage Number Percentage Newcastle Disease Newcastle is one of the serious poultry disease which affected 53,530 households or 59 percent of the total livestock rearing households. The leading districts were, Chakechake with 7,875 households or 74 percent, North B with 5,650 or 67 percent, West and Wete districts, each with 63 percent, Central (58%), North A (49%), Micheweni (48%), Mkoani and South districts, each with 47 percent of the households within the districts (Chart 3.29, Map 3.19). Fowl Typhoid Disease Fowl Typhoid was reported by 20,036 households or 22 percent of the total livestock rearing households. The highest incidence of the problem was reported in Wete of which 3,895 (31 %) of the total households rearing livestock within the district encountered the problem and was followed by West district (30%). The desease was moderately encountered in Micheweni district (24%), Chakechake (23%) and North B (21%). Less than 15% of the cases were reported in the districts of Central (14%), South (12%) and North ‘A’ (12%) (Chart 3.30, Map 3.20). RESULTS Tanzania Agriculture Sample Census - 2007/08 36 RESULTS Tanzania Agriculture Sample Census - 2007/08 37 RESULTS Tanzania Agriculture Sample Census - 2007/08 38 RESULTS Tanzania Agriculture Sample Census - 2007/08 39 3.4.2 Livestock Disease Control Methods The livestock pest control methods focused on Tick problem, Newcastle, Disease and Fowl Typhoid. Livestock diseases were observed in almost all the surveyed districts, with some districts reporting more incidences of particular diseases than in other districts. Since diseases occurrence varies by types of livestock there are differnt control methods for such diseases. This section presents in detail various control methods by type of the Diseases, Type of the Methods and by District. Tick Control Methods Tick borne disease was reported as being the most serious disease infecting ruminants in Zanzibar. The disease was most notorious in Central, Chakechake and Micheweni. The severity of the disease was reported as moderate in Mkoani, Wete, and North B, and low in North A, South and West. Among the Tick control methods reported, spraying was the most commonly method applied by 19 percent of the households in Zanzibar followed by Smearing applied by 8 percent of the livestock raising households. Dipping and other methods accounted to 4 and 2 percents respectively of the livestock raising households applying the method (Table 3.11). There were also variations in the application of these methods by district. Spray methods was practised in all the districts; however the method was most practised in Chake (26%), Central (23%), South (21%), and Mkoani (20%). Smearing was the second most applied tick control method especially in Central (19%), and West (15%). The method was moderately applied in Wete (8%), North B (7%); lowly applied in Micheweni and Chake chake by 5 percent each. Dipping was the least applied method by all the districts in livestock raising households applying the method. There is however the largest percentage (66%) of the livestock raising households reported not to apply any control methods against tick borne disease across the nine surveyed districts (Table 3.15). RESULTS Tanzania Agriculture Sample Census - 2007/08 40 Chart 3.31: Percentage of Households Reporting Newcastle Disease Control Methods Local Herbs, 13,459, 15% Vaccination, 9,594, 10% None, 69,255, 75% Table 3.15 Number and Percentages of Livestock Raising Households practicing various Tick Control methods by District District Dipping Spraying Smearing None Other Total Number % Number % Number % Number % Number % Number % North-A 410 4 819 9 410 4 7,371 81 95 1 9,104 100 North-B 254 3 1,451 17 585 7 6,108 72 76 1 8,475 100 Central 426 5 2,128 23 1,763 19 4,742 52 91 1 9,150 100 South 65 2 845 21 244 6 2,941 72 16 0 4,110 100 West 408 3 2,512 19 1,978 15 8,289 62 157 1 13,345 100 Wete 820 6 2,101 16 974 8 8,430 66 436 3 12,761 100 Micheweni 759 6 2,307 18 701 5 8,760 68 380 3 12,907 100 Chake 186 2 2,821 26 488 5 6,759 63 395 4 10,650 100 Mkoani 696 6 2,321 20 464 4 7,979 68 348 3 11,808 100 Total 4,024 4 17,304 19 7,607 8 61,380 66 1,994 2 92,309 100 Newcastle Control Methods Newcastle problem was a notorious problem which affected poultry. In Zanzibar, incidences of the disease were reported as severe in Chakechake and North B districts, followed by West and Wete with the remaining districts reporting the disease incidence as moderate and slightly severe. The Newcastle control methods practised in Zanzibar were use of Local herbs, practised by 15 percent of the poultry raising households, and vaccination practised by 10 percent of the poultry raising households. Seventy five percent (75%) of the poultry raising households reported not to have practised any Newcastle control methods (Chart 3.31). The application of these two control methods against the Newcastle problem varied from district to district. Local herbs, which was most popular control method in all the districts, was highly practised in North A and South, at 21 percent of the livestock raising households in each of the two districts followed by Central and West districts with 19 percent of livestock raising households in each district. The method was also practised by only 12 percent in each of the livestock raising households of North B, Wete, and Micheweni districts. The method was least practised in Chakechake (9%) followed by Mkoani (10%). RESULTS Tanzania Agriculture Sample Census - 2007/08 41 Chart 3.31: Percentage Livestock rearing Households Practicing Newcastle Control by District 0 15 30 45 60 75 90 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Percentage Vaccination Local Herbs None Vaccination was widely used in West (18%) followed by Central (13%) and moderately used in Wete (11%), Mkoani (10%), and North (9%). The method was less used in North B, South, Wete, and Chakechake districts where only 7 percent of the livestock raising households in each of the districts reported to have been applying vaccination (Chart 3.31). On the whole, the application of vaccination against the Newcastle disease was not as popular as the local herbs and that the districts which applied local herbs most of them had low percentages of livestock raising households using vaccination against the disease. The vice versa was however not the case because the districts with high percentages of livestock raising households which applied vaccination were not necessarily the ones which had low application of local herbs. Therefore, the trend implies that the local herbs were considered as the best alternative, and vaccination was considered as a supplementary method. Generally, Chakechake district had the highest percentage (84%) of households not practicing any Newcastle control method, followed by North B (81%), Mkoani and Wete districts each represented by 80 percent, Micheweni (77%), South (72%), North A (69%), West (68%) of the livestock raising households not to have applied any Newcastle control method (table 3.16 and Chart 3.31). Table 3.16 Number and Percentages of Livestock Raising Households which Practiced Newcastle Control Methods by District District Vaccination Local Herbs None Total Number % Number % Number % Number % North ‘A’ 851 9 1,953 21 6,300 69 9,104 100 North-B 611 7 993 12 6,871 81 8,475 100 Central 1,186 13 1,733 19 6,232 68 9,150 100 South 276 7 877 21 2,957 72 4,110 100 West 2,386 18 2,512 19 8,446 63 13,345 100 Wete 948 7 1,589 12 10,224 80 12,761 100 Micheweni 1,372 11 1,606 12 9,928 77 12,907 100 Chake 760 7 992 9 8,898 84 10,650 100 Mkoani 1,205 10 1,205 10 9,398 80 11,808 100 Total 9,594 10 13,459 15 69,255 75 92,309 100 RESULTS Tanzania Agriculture Sample Census - 2007/08 42 Chart 3.32: Percentage of Households Reporting Fowl Typhoid Control Methods None, 80,809, 88% Vaccination, 2,971, 3% Local Herbs, 8,560, 9% Chart 3.33: Percentage Livestock Rearing Households Controlling Fowl Typhoid by District 0 20 40 60 80 100 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Percentage Vaccination Local Herbs None Fowl Typhoid Control Methods Fowl typhoid was another disease that affected poultry in Zanzibar. The incidences of the disease were reported as severe in Wete, West districts; and moderately in Micheweni, Chakechake and North B districts. Low incidences of the disease were reported in North A, South, Central and Mkoani districts. The control methods for Fowl Typhoid included prophylactic treatment and use of local herbs. Local herbs were more widely practised than vaccination or any other therapeutic approaches against Fowl Typhoid. Local herbs were reported to be used by 8,560 households or 9 percent as compared to 2,971 or 3 percent of the poultry raising households. It is apparent that the number of poultry raising households which applied Fowl Typhoid Control Methods was much lower than that which applied Newcastle Control Method. This implies that the Newcastle disease affected more poultry raising households than did the Fowl Typhoid. This situation is also reflected by higher numbers and percentages of poultry raising households which reported not to have used any Fowl Typhoid Control Methods (Chart 3. 33, Table 3. 13). The trend of application of the Fowl Typhoid Contral Methods by District and by Method Type indicates that Local Herbs was more widely applied in all the districts. However, West had the highest parentage (12%) of the households which applied Local Herbs, followed by Wete and Micheweni districts, each with 11 percent of the households. The method was moderately applied in the districts of North A (10%), South (9%), Mkoani and Central, each with 8 percent, Chakechake (5%) and North B (6%) (Chart 3.36, Table 3.17). RESULTS Tanzania Agriculture Sample Census - 2007/08 43 Chart 3.34: Percentage of Livestock Rearing Households Dewormed Livestock by District 0 10 20 30 40 50 60 Central West North 'B' North 'A' Chakechake South Wete Micheweni Mkoani District Number Chart 3.35: Proportion of Households that Dewormed Cattle by District Micheweni, 13.3 Wete, 12.1 West, 16.7 South, 3.6 Central, 19.4 North 'B', 8.2 North 'A', 5.7 Mkoani, 9.7 Chakechake, 11.2 Table 3.17: Number and Percentages of LRHH which Practiced Fowl Typhoid Control Methods by District District Vaccination Local Herbs None Total Number % Number % Number % Number % North ‘A’ 347 4 945 10 7,812 86 9,104 100 North-B 153 2 484 6 7,839 92 8,475 100 Central 274 3 699 8 8,207 89 9,180 100 South 81 2 390 9 3,639 89 4,110 100 West 848 6 1,664 12 10,833 81 13,345 100 Wete 461 4 1,461 11 10,839 85 12,761 100 Micheweni 292 2 1,431 11 11,184 87 12,907 100 Chake 248 2 496 5 9,906 93 10,650 100 Mkoani 268 2 991 8 10,550 89 11,808 100 Total 2,971 3 8,560 9 80,809 88 92,339 100 3.4.3 Deworming Practices Deworming was generally practiced in all the districts for cattle, goats, sheep, pigs, and chicken. There was however some variations on the extent of the practice by district and by type of livestock. Deworming was practiced in the districts as follows: Central (52%), West (45%), North B (31%), North A (29%), Chakechake (28%), South (27%), Wete (24%), Micheweni (24%) and Mkoani (19%) of the total livestock rearing households with the districts (Chart 3.34). Deworming of Cattle Deworming of cattle was practiced by households in all the districts. A total of 17,084 households were reported to dewomed cattle and this represents 60.8 percent of the total household that dewomed livestock. Deworming was mostly practiced in Central and West districts with 19.4 and 16.7 percents respectively. This was followed by Micheweni (13.3%) and Wete (12.1%). RESULTS Tanzania Agriculture Sample Census - 2007/08 44 Chart 3.36: Proportion of households that Dewormed Goats/Sheep by District North 'A', 8 South, 8 Wete, 6 Mkoani, 5 Central, 22 West, 17 North 'B', 13 Micheweni, 10 Chakechake, 11 Chart 3.37 Proportion of Households that Dewormed Pigs by District Central, 75 West, 100 Deworming was moderately practiced in the districts of Chakechake by (11.2%) and Mkoani (9.7%) Dewoming of cattle was lowly practiced in the districts of South with (3.6%), North ‘A’ (5.7%) and the least district was North ‘A’ with only 5.7 percent of the total household dewoming cattle (Chart 3.35). Deworming of Goats and Sheep Deworming was practiced for goats sheep in all the districts. The result reveals that a total of 4,109 households reported to dewormed Goats/sheeps and this represent 15 percent of the total livestock rearing households deworming livestock. However, deworming for goats/sheep was mostly practised in Central 912 (22%), West 691(17%), North ‘B’ 534(13%), Chakechake 457(11%) and this was followed by Micheweni with 409(10%). The method was moderately practiced in North ‘A’ 347(8%), Wete (6%) and Mkoani (5%) (Chart 3.36, Table 3.18). Deworming of Pigs Deworming of Pigs was practiced by only two out of nine of the surveyed districts. A total of 122 households rearing pigs were reported to deworm pigs and this represent 80 percent of the total pigs rearing households. The districts which practised deworming of pigs include; Central and West. All householhs rearing pigs in West district were reported to deworm pigs. In Central district the deworming pigs households accounted for 75 percent, the remaining 25 percent within the district were reported not to deworm pigs(Table 3.19). RESULTS Tanzania Agriculture Sample Census - 2007/08 45 Table 3.18: Number of Goats/Sheep Rearing Households which Dewormed Goats/Sheep by District District Deworming Goats/Sheep Not Deworm Goats/Sheep Not Applicable Number of Goats/Sheep Rearing households Number % Number % Number % North ‘A’ 347 8 410 11 1,890 9 2646 North-B 534 13 102 3 2,112 10 2749 Central 912 22 912 25 3,009 14 4833 South 341 8 211 6 666 3 1218 West 691 17 502 14 4,773 22 5966 Wete 231 6 359 10 2,486 12 3075 Micheweni 409 10 555 15 2,394 11 3358 Chake Chake 457 11 186 5 2,325 11 2969 Mkoani 187 5 402 11 1,740 8 2329 Total 4,109 100 100 21,396 100 29,143 The was no household which raised pigs in North ‘B’, North ‘A’, South and all districts in Pemba. Table 3.19 Number of Livestock Rearing Households which Dewormed Pigs by District District Deworming Pigs Not Deworm Pigs Total Number % Number % Number of Pigs Rearing households % North ‘A’ 0 0 0 North-B 0 0 0 Central 91 75 31 25 122 100 South 0 0 0 0 0 0 West 31 100 0 0 31 100 Wete 0 0 0 0 0 0 Micheweni 0 0 0 0 0 0 Chake Chake 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 Total 122 80 31 20 153 100 Deworming of Chicken Chicken demorming was found to be a common practice by most of the chicken raising households in all the districts. The practice was however, more popular in West of which 3,360 household (27%) and Central with 1885 (15%) of the total household dewormed chicken. The practice was moderately practiced in North B and North ‘A’ with 1,222 (10%) and 1481(12%) of the households respectively. Mkoani had 1,151 (9%), Wete (10%), Chakechake (6%) and the least district was South with 487(4%) of the chicken raising households that dewormed. (Chart 3.38, Table 3.20). RESULTS Tanzania Agriculture Sample Census - 2007/08 46 Chart 3.38 Proportion of Livestock Rearing Households Deworming Chicken by District Micheweni, 7 Wete, 10 West, 27 South, 4 Central, 15 North-B, 10 North ‘A’, 12 Chakechake, 6 Mkoani, 9 Table 3.20 Number of Livestock Rearing Households which Dewormed Chicken by District District Deworming Chicken Not Deworming Chicken Not Applicable Total Number of LRHH Number % Number % Number % North ‘A’ 1,481 12 945 7 567 11 2,993 North-B 1,222 10 942 7 611 12 2,774 Central 1,885 15 2,067 14 1,094 21 5,046 South 487 4 699 5 260 5 1,446 West 3,360 27 1,915 13 1,068 20 6,343 Wete 1,281 10 2,332 16 333 6 3,946 Micheweni 934 7 2,570 18 526 10 4,030 Chake Chake 806 6 1,635 11 496 9 2,938 Mkoani 1,151 9 1,205 8 295 6 2,651 3.5 Bee Keeping Bee keeping was also practised in Zanzibar, but not as widely as in the Mainland. Only one percent of the total agricultural households were involved in honey production. District-wise, South and Micheweni districts had the highest percentages of households involved in honey production at 3.7 percent and 2 percent respectively. Honey production was moderately practised in Mkoani (1.7%), Wete (1.2%), Chakechake and Central.each with 0.7 percent, North B and West, each with 0.2 percent of the total agricultural households within the districts. However, the activity was reported not to have been practised in North A (Table 3.21). RESULTS Tanzania Agriculture Sample Census - 2007/08 47 Table 3.21: Number of Agricultural Households Involved in Honey Production by District District Agricultural Households Involved in Honey Production/Collection Agricultural Households NOT Involved in Honey Production/Collection Total Number % Number % Number % North ‘A’ 0 0.0 18,901 100.0 18,901 100.0 North-B 25 0.2 11,427 99.8 11,452 100.0 Central 91 0.7 13,588 99.3 13,679 100.0 South 244 3.7 6,336 96.3 6,580 100.0 West 31 0.2 18,620 99.8 18,651 100.0 Wete 179 1.2 15,195 98.8 15,374 100.0 Micheweni 350 2.0 17,170 98.0 17,520 100.0 Chakechake 93 0.7 13,742 99.3 13,835 100.0 Mkoani 268 1.7 15,931 98.3 16,199 100.0 Total 1,282 1.0 130,911 99.0 132,193 100.0 3.5.1 Beehives by Type of Bees The survey results show that, there were a total of 87,725 beehives out of which, 62,797 (72%) were of improved type and the remaining 24,928 (28%) were local beehives, both types kept stingless and sting bees. Chakechake had the highest percentage of improved beehives (97%) followed by Central (20%) and Mkoani (17%). Micheweni had the lowest percentage of improved hives (7%). As for local beehives, South, West and Wete districts had the highest percentage of local beehives of which all the beehives are locally made (100%) and kept both stringless and sting bees. Other district with highest percentage includes Micheweni and Mkoani with 93 and 83 percents respectively. The least proportion of number of local beehives was recorded in Chakechake district with (3%). All the sting bees were kept in local beehives. West and Wete districts kept sting bees only. However, Micheweni was the second leading district with 69 percent of the total local beehives. Mkoani was the third leading district with 63 percent followed by South with 21 percent and Central with 20 percent of the local beehives (Table 3.22). RESULTS Tanzania Agriculture Sample Census - 2007/08 48 Table 3.22: Number of Beehives by Type and District strict Stingless Bees Sting Bees Total Improved Beehives Local Beehives Improved Beehives Local Beehives Number % Number % Number % Number % North-B 0 0 0 0 0 0 0 0 0 Central 152 20 456 60 0 0 152 20 760 South 0 0 12,055 79 0 0 3,249 21 15,304 West 0 0 0 0 0 0 628 100 628 Wete 0 0 0 0 0 0 1,230 100 1,230 Micheweni 263 7 905 24 0 0 2,599 69 3,767 Chakechake 62,007 97 0 0 0 0 1,860 3 63,867 Mkoani 375 17 428 20 0 0 1,366 63 2,169 Total 62,797 72 13,844 16 0 0 11,084 13 87,725 3.5.2 Quantity of Honey Harvested and Average Prices The quantity of honey harvested from sting bees was slightly higher (22,262 lts or 54%) than that harvested from stingless bees (19,087 lts or 46%). Also, the quantity of sting bees honey sold was slightly higher (17,807lt or 51%) than that sold from stingless bees (17,084lt or 49%). This imply that the quantity of honey harvested was directly proportional to the quantity of honey sold; that is, the higher the quantity harvested the higher the quantity sold (Table 3.19). On average, honey from Chakechake was the highest priced (at Tshs. 9,999 per litre) than that from other districts; the second highest priced honey was sold in West (at Tshs. 8,000 per litre). Micheweni, Mkoani and South districts sold their honey at moderate prices of Tshs. 7,612 per litre, Tshs. 7,510 per litre, and Tshs.6, 651 per litre respectively (Table 3.23). Table 3.23: Quantity of Honey Harvested and Sold by Type of Bees and District District Stingless Bees Sting Bees Total Honey Harvested Honey Sold Honey Harvested Honey Sold Honey Sold (lts) Honey Harvested (lts) Quantity (lts) % Quantity (lts) % Quantity (lts) % Quantity (lts) % North-B 0 0 0 0 0 0 254 100 254 0 Central 608 71 608 71 243 29 243 29 851 851 South 12,672 53 12,640 55 11,161 47 10,495 45 23,135 23,834 West 0 0 0 0 1,884 100 0 0 0 1,884 Wete 51 4 0 0 1,230 96 922 100 922 1,281 Micheweni 3,533 55 1,694 48 2,862 45 1,840 52 3,533 6,395 Chakechake 0 0 0 0 1,240 0 1,240 0 1,240 1,240 Mkoani 2,222 38 2,142 43 3,641 62 2,811 57 4,953 5,864 Total 19,087 46 17,084 49 22,262 54 17,807 51 34,890 41,349 RESULTS Tanzania Agriculture Sample Census - 2007/08 49 Table 3.24: Average Prices of Honey (Tshs /litre) by Type of Bees and by District The price of honey was lowest in North B and was sold at Tshs. 3,000 per litre, followed by Central and Wete districts which sold their honey at Tshs. 3,500 per liter and Tshs. 4,429 per litre respectively. On average, honey from stingless bees was sold at higher prices than that from sting bees in all districts except South district which sold its honey from sting bees at a higher price than the honey harvested from stingless bees. Districts which had higher prices per litre from stingless bees include; Mkoani (at Tshs. 7,500 per litre), Micheweni (at Tshs. 5,400 per litre) and Central (at Tshs. 5,000 per litre) (Table 3.24). Table 3.25: Number of Agricultural Households by Location and Honey Outlets 3.5.3 Honey Outlets by Location and Region In terms of outlets, neighbours were the major outlet for the produced honey as reported by 894 (66%) of the total households that produced/collect honey. The local markets were second largest outlet reported by 121 (9%) followed by trade at farms 109 (8%) and secondary markets 63 (4.7 per cent of the total households that produced/collect honey). A total of 161(12%) households that produced/collect honey were reported not to have sold honey to any outlet (Table 3.25). District-wise, Micheweni had more households 321 or 27 percent of the total households which sold honey this was followed by Mkoani with 295 (25%), South district account 260 (22%). Wete District Stingle ss Bees (Price per Litre) Sting Bees (Price per Litre) Average Price Per Litre North-B 0 3,000 3,000 Central 5,000 1,000 3,500 South 2,033 5,635 6,651 West 0 8,000 8,000 Wete 0 4,429 4,429 Micheweni 5,400 4,912 7,612 Chakechake 0 9,999 9,999 Mkoani 7,500 3,760 7,510 Total 14,560 25,687 25,955 Outlet Number of Households % Neighbours 894 66 Local markets 121 9 Secondary markets 63 5 Processing industries 0 0 Large scale farms 0 0 Trade at farms 109 8 Did not sell 161 12 RESULTS Tanzania Agriculture Sample Census - 2007/08 50 Chart 3.39: Percentage of Households selling Honey by District Central, 8 North 'B', 2 West, 3 Wete, 9 Micheweni, 27 Chakechake, 5 Mkoani, 25 South, 22 Chart 3.40 Number and Percentage of Households Receiving Livestock Extension Advice by District 0 1,000 2,000 3,000 4,000 5,000 6,000 North 'A' North 'B' Central South West Wete Micheweni Chakechake Mkoani District Number 0 5 10 15 20 25 Percentage Number Percent Chart 3.41 Percentage Distribution of Source of Extension Advice Cooperative, 5 NGO/Dev project, 16 Government, 59 Large scale farmer, 14 Radio/TV/News papers, 19 Neighbour, 22 and Central districts had moderate number of households that sold honey with 102(9%) and 91(9%) respectively. The lowest number of household that reported to sell honey was in North ‘B’ and West districts of which accounted 2 and 3 percents (Chart 3.39). 3.6 Access to Extension Services by District In Zanzibar, all the districts were observed to have received livestock extension messages. The survey shows that, a total of 23,336 households received extension services presenting 26 percent of all the livestock keepers in Zanzibar (91,380 households). West district had the highest number of households (5,212 or 22.3 percent) receiving extension services, followed by Central district 3,921(16.8%), North ‘B’ 3,258 (14%), Mkoani 2,374 (10.2%), Wete 2,306 (9.9%), North A 2,268 (9.7%), South district 1,072 (4.6%), Chakechake 1,318 (5.6%) and Micheweni 1,606 (6.9%) (Chart 3.40). 3.6.1 Sources of Extension Services The main source of livestock extension services was the Government accounting for 59 percent of the households which received advices. Other sources of advice came from neighbours (22%), Non Governmental Organizations (NGOs) and Development Projects (16%), Large Scale Farmers (14%), Radios/TVs/Newspapers (19%) and Cooperatives (5%) (Chart 3.41). RESULTS Tanzania Agriculture Sample Census - 2007/08 51 Chart 3.42 Number of Household Receiving Extension Advice on Disease Control by District 9 12 19 5 21 11 6 5 10 0 5 10 15 20 25 North 'A' Kaskazini-B North 'B' South West Wete Micheweni Chakechake Mkoani District Number 3.6.2 Extension Advice by Type of Messages Extension Advice by Type of Messages provided included: proper feeding, advice on housing, proper milking and milk hygiene, disease control, pasture establishment, group formation, calf rearing, use of improved bulls, and livestock feeds processing. Of these messages, disease control was provided to 21 percent of the households. Extension messages on feeds and proper feeding were provided to 9,615 (12%) and housing messages were provided to 9,222 (12%) households. Fewer households were provided with advice messages on use of improved bulls (6%), livestock feed processing (7%), proper milk hygiene (8%) and group formation (9%) (Table 3.26). 3.6.3 Number of Households which Received Advice Messages on Disease Control As observed earlier, livestock diseases and the high rate of livestock infections were serious problems encountering livestock raising households. Advice on disease control was therefore very critical. Table 3.26 Percentage of Households which Received Extension Advice by Type of Message . Most of the households received advice on disease controls. The situation was as follows: West 3391(21%), Central 3070 (19%), North ‘B’ 2,111(12%), Wete 1,794(11%), Mkoani 1,669(10%), Micheweni 1,022(6%). The district with smallest number of households that received advice on disease control were South and Chakechake with 5 percents each (Chart 3.42). Advice Number of Households Percentage Feeds and Proper Feeding 9,615 12 Advice on Housing 9,222 12 Proper Milking and Milk Hygiene 5,984 8 Livestock Fattening 4,535 6 Disease Control 16,150 21 Herd/Flock Size 4,479 6 Pasture Establishment 2,823 4 Group Formation 7,108 9 Calf Rearing 7,441 10 Improved Bulls 4,714 6 Livestock Feeds Proccessing 5,451 7 CONCLUSIONS Tanzania Agriculture Sample Census - 2007/08 52 CONCLUSIONS The livestock sector analysis focused mainly on livestock numbers by specie, district livestock distribution, productivity, livestock diseases, access to services and contribution to crop production. Data for the 2007/08 Agricultural Sample Census is compared with the previous census data so as to identify any structural changes within the districts between the census periods. The main livestock species kept by smallholder farmers include cattle, goats, sheep, pigs and chicken. In the 2007/08 Agricultural Sample Census, there were about 132,959 households which kept livestock of which, 39,420 (29.6%) households kept cattle, 80,069 (60.2%) households kept chicken, 13,107 (9.9%) households kept goats. Pigs and sheep keeping households were 363 (0.3%) in total. In the surveyed households, chicken were the most dominant specie with 1,078,962 (82.5%) flocks followed by cattle (155,624 (11.9%) herds, goats 68,972 (5.3%) herds, donkeys and pigs. The proportion of households which kept donkeys, sheep and pig population were in total less than one percent. The total number of cattle raised by the smallholders was 155,624 heads out of which, the indigenous type represented 95.5 percent of the total cattle population. On average, the herd size per cattle holding in the smallholder sector was 4 heads. Central district followed by Micheweni, West and Wete were the leading districts in terms of cattle populations. In the five year period between 2003 and 2008, the total cattle population among the smallholders decreased by 4.5 percent from 162,643 to 155, 624 heads giving an annual negative growth rate of about 0.9 percent per annum over the five year period. Central district had the highest concentration of dairy cattle (29.7%) of the total dairy cattle population compared to other districts. The average number of goats per household was 5 goats, the number has decreased by 9 percent compared to 2002/03 Agricultural Sample Census. Most of the households (97%) raised between 1 and 14 goats representing 74 percent of the total goat population. Among the districts, Central, West, North ‘A’ and Mkoani had the highest number of goats. The number of goats has increased by 30 percent between 2003 and 2008 with an annual growth rate of about 6 percent. Unlike the goat population increase between 1993 and 2003, sheep population declined by 53 percent in the same period. However, the trend was reversed between 2003 and 2008, whereby the number of sheep increased from 300 to 574 representing an increase of 91 percent. For pigs, the trend was positive although the increase was small. CONCLUSIONS Tanzania Agriculture Sample Census - 2007/08 53 About 75 percent of the entire chicken population was kept by 96 percent of the households whereby the flock size was in the range between 1 and 49. The chickens were mainly of indigenous/ local type. The leading districts in terms of number of chicken were West (21.3%), Wete (13.8%), Mkoani (12.4%), North ‘B’ (11.2%) Chakechake (9.9%) and South (4.8%). These districts accounted for 73.4 percent of the total chicken population. Most of the milk (99%) was from cows and production during the wet season was 111,616 litres per day which dropped to 84,383 litres per day during the dry season. Average milk production per cow was 2.5 litres during the wet season and 2.3 litres during the dry season. The number of milked cows has also dropped from 44,718 during the wet season to 36,639 in the dry season. The main milk producing districts were West (23%), Central (19%), Wete (13%) and North ‘B’(14%). The four districts produced 69 percent of the total milk production in Wet season. Contribution of livestock to crop production was very small as measured by the proportion of planted area using organic fertilizers. Only 7% of all households planting during Long rain used organic fertilizers and the area planted with organic fertilizers was only 7.8 percent. There were differences in the extent to which manure were used among districts. Furthermore the proportion of land applied did not correlate with the number of livestock owned, probably because most of the livestock are indigenous, and grazed on communal land, Common diseases which affected the ruminants include Tick Borne Disease (TBD), Foot and Mouth Disease (FMD) and Lumpy Skin Disease. Almost 76 percent of the cattle raising households encountered Tick Borne Disease. The problem was more serious in Central district followed by Chakechake, Micheweni and Mkoani. Spraying with acaricides was the most common method used to control the infections. Dipping and smearing was however, less practiced. For chicken, Newcastle Disease and Fowl Typhoid were reported to remain as a challenge. Access to livestock extension services was critical taking into consideration the widespread nature of the livestock diseases and the high rates of livestock infection. All the districts were observed to have received livestock extension messages. The main source of livestock extension services was the Government with about 59 percent of the households which received advice. Livestock diseases and the high rates of livestock infections were the serious problems encountered by the livestock raising households. CONCLUSIONS Tanzania Agriculture Sample Census - 2007/08 54 Access to extension services varied within the districts. West district had the highest number (22.3%) of the households which received regular extensions services followed by Central (16.7%) and North ‘B’(14%). Chakechake and South districts had the least access to extension services of which jointly account only 10 percent. The government provided most of the needed extension services. Advice on disease controls was therefore very critical. District Profiles The following District Profiles summerize the status of Livestock Production in each district. Central Central district kept most of the cattle, particularly the improved dairy type. It also had more households with goats and higher number of goat population than any other district. The district also kept most of the pigs (63%) of the total pig population followed by West. However, the district had higher incidences of TBD, Lumpy sSkin disease, FMD and Worm infestations. Central district by its location had more access to extension services particularly from the government. Micheweni This district was second best in the number of livestock and ranked highest in the number of indigenous cattle. The proportion of dairy cattle was less than 2percent. It ranked third in terms of number of sheep. FMD was less than 2 percent. Incidences of TBD were high and the district was third in TBD cases after Central and Chake Chake. In terms of extension services, the district had relatively the lowest number of households which received extension services than any other district. Similary, the district had also received fewer advices on livestock disease control than Central and West. Wete Wete disrict ranked third in the number of livestock, number of cattle, improved dairy cattle and was also third in terms of number of households which kept improved dairy types. The district was second best in the number of chicken, especially the indigenous type. Incidence of TBD was 33 percent of the total livestock rearing households within the district. Access to extension services was moderate and was comparable to Micheweni and Chakechake(less than20%) and advices on disease was also modest (13.4%) compared to Central district in which 15.3 percent of the households reported to have received such advices from the government. CONCLUSIONS Tanzania Agriculture Sample Census - 2007/08 55 West West district ranked first in the number of sheep followed by Central and Micheweni. However, the district was second best in the number of improved dairy cattle, number of households keeping dairy cattle and number of goats. It produced more milk than other districts followed by Central, Wete and North ‘B’ disrticts. It also ranked first in the number of chicken (21.3%) of the total chicken population and was second in number of pigs (37%). The access to extension services was generally good and the district had the highest number of households 5,212 (22.3%) with access to extension services. North –‘A’ North –A district kept the least number of livestock compared to other districts. It was second with smallest number of cattle, both indigenous and improved types. About 11 percent of the households kept 12 percent of the goats.. The district also had the lowest number of sheep (6%) and kept about 10 percent of the total chicken population. With regard to disease incidences, the districtNorth ‘A’ encountered less livestock diseases of all types in most of the livestock rearing households. Newcastle was moderate with about 30 percent of the agricultural households. A large number of households did not control Tick Borne disease and only 6 percent of the households did vaccination against the Newcastle disease. There was absolutely no Honey production and the district received little extension advices. North-‘B’ North –‘B’ district had few livestock just like North ‘A’. It was the third with lowest number of cattle in Zanzibar and only 4.2 percent of the households kept 3.7 percent of the improved dairy cattle. The number of goat and sheep number was moderately low and also, the district raised 9.5 percent of the chicken population. Disease incidences were low compared to other districts. Only 13 percent of the households reported incidences of Lumpy Skin disease, Tick Borne (3%), FMD (8%). Relatively, there were high incidences of Newcastle disease (slightly greater than60%) and about 22 percent of the households did not control the disease. The district was third highest with proportion of households which dewormed their livestock and was first in terms of sheep and goat deworming. However, extension services were very poor. CONCLUSIONS Tanzania Agriculture Sample Census - 2007/08 56 South Livestock keeping was not important in South district compared to other districts. The district had the least number of cattle, sheep and, goats. There is no household recorded to kept pigs.Very few (0.7%) dairy cattle were raised and the district was the second with lowest number of households which raised goats. As a result, disease incidences and extension services on the livestock were lowest. However, the district ranked highest in terms of honey production and honey sales. Chakechake The district had a moderate number of cattle sheep and goats. About 94 percent of the cattle population were of indigenous type. Households owned few goats, sheep and chicken. Tick Borne problem was reported by about 50 percent of the households, while incidences of other diseases were lower. The district was the third highest in the number of households which dewormed their cattle and second highest in the number of households which dewormed their sheep.. Honey production was moderate though the district had the highest number of stingless beehives. However, the average price of honey was highest in the district and livestock extension services were the second highest, but with moderate low advices on the livestock diseases. Mkoani The district had a moderate number of cattle (16,976 heads). Most (99%) of the cattle were of indigenous type . Goat population was fourth highest, though the district ranked second in terms of number of households rearing goats. The district was the third largest with number of households which kept indigenous chicken. The district had the moderate incidences of Tick Borne infection. In terms of Helminth control, it had the highest number of households which dewormed their cattle but was the least in sheep and goat deworming. Extension services were moderate and the district had few households engaged in honey production. APPENDICES Tanzania Agriculture Sample Census - 2007/08 57 5. APPENDICES Appendix I: Livestock and Poultry Tabulation List Appendix II: Livestock and Poultry Tables Appendix III: Questionnaires APPENDIX I Tanzania Agriculture Sample Census - 2007/08 58 Appendix I: Livestock and Poultry Tabulation List Table Number Description Page TYPE OF AGRICULTURE HOUSEHOLD Table 2.1 Number of Households by Type of Household and District during 2007/08 Agriculture year ………………………………………………….65 Table 2.2 Number of Agriculture Households by type of Holding by District during 2007/08 Agriculture year ........................................................................................... 65 Table 2.3 Number of Agriculture Households by Type and Size of Holding, 2007/08 Agricultural Year ....................................................................................................... 66 LIVESTOCK CONTRIBUTION TO CROP PRODUCTION Table 2.4 Number of Households and Planted Area by Organic Fertiliser Use and District SHORT RAINY SEASON .................................................................... 66 Table 2.5 Number of Households and Planted Area by Organic Fertiliser Use and District - LONG RAINY SEASON .................................................................... 67 CATTLE PRODUCTION Table 9.1.1 Total Number of Households Rearing Cattle by District during 2007/08 Agriculture Year ........................................................................................................ 67 Table 9.1.2 Number of Cattle by Type and District as of 1st October 2008 ................................ 68 Table 9.1.3 Number of Households rearing cattle, Head of Cattle and Average Head per Household by Herd size During the 2007/08 Agricultural Year ................ 68 Table 9.1.4 Total Number of Cattle by Cattle Types and Category, 2007/08 Agricultural Year ....................................................................................................... 68 APPENDIX I Tanzania Agriculture Sample Census - 2007/08 59 Table 9.1.5 Total Number of Indigenous Cattle by Category of Cattle and District During the 2007/08 Agricultural Year ....................................................................... 69 Table 9.1.6 Total Number of Improved Diary Cattle by Category of Cattle and District During the 2007/08 Agricultural Year .......................................................... 69 Table 9.1.7 Total Number Households rearing Cattle and Method of Cattle Identification by District during, 2007/08 Agricultural Year .................................... 70 MILK PRODUCTION Table 9.2.1 Number of Milked Cows by Category of Cattle, Season and District, During the 2007/08 Agricultural Year ....................................................................... 71 Table 9.2.2 Average milk production per cow per day, by Category of Cow, Season and District, During the 2007/08 Agricultural Year ................................................. 71 Table 9.2.3 Average number of days for cows on milked, by category of Cattle, Season and District, During the 2007/08 Agricultural Year ...................................... 71 Table 9.2.4 Average Cattle Milk price (Tshs/litre) per season by category of cow and District, During the 2007/08 Agricultural Year .................................................. 72 GOAT PRODUCTION Table 9.3.1 Number of Agriculture Households Rearing Goats by District during ... the 2007/08 Agricultural Year ................................................................................... 72 Table 9.3.2 Number of Goats by Type and District as of 1st October 2008 ................................ 72 Table 9.3.3 Number of Households rearing Goat, Head of Goat and Average Head per Household by Herd size During the 2007/08 Agricultural Year ......................... 73 Table 9.3.4 Total Number of Goats by Category and Type of Goat as of 1st October 2008 ............................................................................................................. 73 APPENDIX I Tanzania Agriculture Sample Census - 2007/08 60 Table 9.3.5 Total Number of Goats by Category and Type of Goat as of 1st October 2008 ............................................................................................................. 73 Table 9.3.6 Number of Improved Goats for Meat by Category and District as of 1st October 2008 ............................................................................................................. 74 Table 9.3.7 Number of Improved Dairy Goats by Category and District as of 1st October 2008 ............................................................................................................. 74 Table 9.3.8 Number of Milked Goat by Category of Goat, Season type and District, During the 2007/08 Agricultural Year ................................................. 75 SHEEP PRODUCTIO N Table 9.4.1 Total Number Households Rearing Sheep by District during, 2007/08 Agricultural Year ....................................................................................................... 75 Table 9.4.2 Number of Household Rearing and number of Sheep by Type and District as of 1st October 2008 .................................................................................. 75 Table 9.4.3 Number of Indigenous Sheep by Category of Sheep and District as of 1st October 2007/08 Agriculture year ............................................................................ 76 Table 9.4.4 Number of Households rearing Sheep, Head of Sheep and Average Head per Household by Herd size During the 2007/08 Agricultural Year ......................... 76 PIG PRODUCTION Table 9.5.1 Number of Households Raising Pigs by Districts during 2007/08 Agriculture Year ........................................................................................................ 76 Table 9.5.2 Number of Households Rearing Pigs, Head of Pigs and Average Head per Household by Herd Size as of 1st October 2008 ................................................. 77 Table 9.5.3 Total Number of Pigs by Type of Pigs and District as of 1st October 2008 ............. 77 APPENDIX I Tanzania Agriculture Sample Census - 2007/08 61 CHICKEN PRODUCTION AND OTHER LIVESTOCK Table 9.6.1 Total Number of Pigs by Type of Pigs and District as of 1st October 2008 ............. 77 Table 9.6.2 Number of Households Keeping Chickens and Average Number of Chickens per Household by Flock Size as of 1st October 2008 ................................................ 78 Table 9.6.3 Number of Other Livestock by Type of livestock by District as of 1st October 2008 ............................................................................................................. 78 Table 9.6.4 Number of Households Keeping Other Livestock and Average Number per Household by Flock Size as of 1st October 2008 ................................................ 78 Table 9.6.5 Total Number of Other Livestock by Type as of 1st October 2008 .......................... 79 LIVESTOCK PESTS & PARASITE CONTROL Table 9.7.1 Number of Livestock Rearing households deworming Livestock by District during 2007/08 Agriculture Year ............................................................................... 79 Table 9.7.2 Number of Livestock Rearing households that dewormed Livestock by type of livestock and District, 2007/08 Agricultural Year ....................................... 80 Table 9.7.3 Number of Livestock Rearing Households Normally Encountering Tick Problems by District during 2007/08 Agriculture Year ............................................ 81 Table 9.7.4 Number of Livestock Rearing Households by Method of Tick Control and District during 2007/08 Agriculture Year Year .................................................. 81 Table 9.7.5 Number of Livestock Rearing Households normally Encountering Newcastle Disease Problems by District during 2007/08 Agriculture Year ............. 82 Table 9.7.6 Number of Livestock Rearing Households by Method of Newcastle Disease Control by District during 2007/08 Agriculture Year .................................. 82 APPENDIX I Tanzania Agriculture Sample Census - 2007/08 62 Table 9.7.7 Number of Livestock Rearing Households normally Encountering Fowl Typhoid Disease Problems by District during 2007/08 Agriculture Year ........................................................................................................ 82 Table 9.7.8 Number of Livestock Rearing Households by Method of Fowl Typhoid Disease Control by District during 2007/08 Agriculture Year ................... 83 Table 9.7.9 Number of Livestock Rearing Households normally Encountering Foot and Mouth Disease Problems by District during 2007/08 Agriculture Year ............. 84 Table 9.7.10 Number of Livestock Rearing Households Normally Encountering Lympyskin Disease Problems by District during 2007/08 Agriculture Year ............ 83 LIVESTOCK EXTENSION Table 9.8.1 Number of Households Receiving Extension Advice by District during the 2007/08 ............................................................................................................... 84 Table 9.8.2 Number of Households receiving Livestock advice (overall) By Source of Extension and District during the 2007/08 agriculture year ...................................... 85 Table 9.8.3 Number of Households receiving Livestock advice (overall) By Source of Extension and District during the 2007/08 agriculture year ...................................... 85 Table 9.8.4 Number of households receiving Extension Advice on ProperLivestock Housing by District during the 2007/08 Agriculture Year ....................................... 86 Table 9.8.5 Number of households Receiving Extension advice on Proper Milking and Milk Hygene by District during the 2007/08 Agriculture year........................... 86 Table 9.8.6 Number of households Receiving Extensionadvice on Livestock fattening by District during the 2007/08 Agriculture Year ....................................... 87 Table 9.8.7 Number of households receiving extension advice on Disease control (dipping/spraying) by District during the 2007/08 Agriculture year ......................... 87 APPENDIX I Tanzania Agriculture Sample Census - 2007/08 63 Table 9.8.8 Number of households Receiving Extension Advice on Herd/Flock size and Selection by District during the 2007/08 Agriculture Year ................................ 88 Table 9.8.9 Number of households Receiving Extension Advice on Pasture Establishment by District during the 2007/08 Agriculture Year ............................... 88 Table 9.8.10 Number of Households Receiving Extension Advice on Group formation and Strengthening by District during the 2007/08 Agriculture year ......................... 89 Table 9.8.11 Number of Households Receiving Extension Advice on Calf Rearing by District during the 2007/08 Agriculture Year ....................................................... 89 Table 9.8.12 Number of Households Receiving Extension Advice on Use of Improved Bulls by District during the 2007/08 Agriculture Year ............................ 90 Table 9.8.13 Number of Households Receiving Extension Advice on Livestock Feeds Processing by District during the 2007/08 Agriculture Year .......................... 91 FISH FARMING Table 9.9.1 Number of Agriculture Households Practising Fish Farming by District during the 2007/08 Agriculture Year ......................................................................... 92 Table 9.9.2 Number of Agriculture Households by System of Fish Farming and District during the 2007/08 Agriculture Year ............................................................ 92 Table 9.9.3 Number of Agriculture Households by Source of Fingerling by Districts during the 2007/08 Agriculture Year ......................................................................... 92 Table 9.9.4 Number of Agriculture Households by Location of Selling Fish and District during the 2007/08 Agriculture Year ............................................................ 93 Table 9.9.6 Number of Agricultural Households By frequency of stocking of Fingerings in fish ponds and District, 2007/08 Agricultural Year ............................ 93 APPENDIX I Tanzania Agriculture Sample Census - 2007/08 64 Table 9.9.7 Number of Agricultural Households By level of Care of fish Ponds by District, 2007/08 Agricultural Year .......................................................................... 93 BEE KEEPING Table 9.10.1 Number of Agricultural Households involved in Honey Production/ Collection and District, 2007/08 Agricultural Year .................................................. 94 Table 9.10.2 Number of Agricultural Households By Honey production/Collection and District, 2007/08 Agricultural Year .................................................................... 94 Table 9.10.3 Number of Agricultural Households, type of bee Hives and Type of Bees by District, 2007/08 Agricultural Year ............................................................. 95 Table 9.10.4 Quantity of Honey Harvested and Sold by Size of Bees and District during the 2007/08 Agriculture Year ......................................................................... 95 Table 9.10.5 Average price of Honey (Tshs/litre) by Size of Bees and District during the 2007/08 Agriculture Year .................................................................................... 96 Table 9.10.6 Number of Agriculture Households by Location of Selling Honey and District during the 2007/08 Agriculture Year ..................................................... 96 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 65 APPENDIX II: LIVESTOCK AND POULTRY TABLES 2.1. TYPE OF AGRICULTURE HOUSEHOLD: Number of Households by Type of Household and District during 2007/08 Agriculture year District Rural Households involved in Agriculture Rural households NOT Involved in Agriculture Total Rural Households Number of Urban Households Total Number of Households Number % Number % Number % Number % Number North 'A' 18,901 14.3 0 0.0 18901 13.8 1,286 6.37 20,187 North 'B' 11,452 8.7 0 0.0 11452 8.3 1,873 14.1 13,325 Central 13,679 10.3 473 9.2 14152 10.3 646 4.4 14,799 South 6,580 5.0 498 9.6 7078 5.2 917 11.5 7,995 West 18,651 14.1 2,830 54.8 21481 15.6 19,111 47.1 40,592 Wete 15,374 11.6 404 7.8 15778 11.5 6,277 28.5 22,055 Micheweni 17,520 13.3 410 7.9 17930 13.1 1,740 8.8 19,671 Chakechake 13,835 10.5 311 6.0 14146 10.3 4,807 25.4 18,953 Mkoani 16,199 12.3 237 4.6 16436 12.0 2,918 15.1 19,355 Total 132,193 100.0 5,163 100.0 137356 100.0 39,576 22.4 176,932 2.2 TYPE OF AGRICULTURE HH: Number of Agriculture Households by type of Holding by District during 2007/08 Agriculture year District Crops Only Livestock Only Pastoralist Crops & Livestock Total Number of Households Total Number of Households Growing Crops Total Number of Households Rearing Livestock Number % Number % Number % Number % North 'A' 16,318 86 126 1 0 0.0 2,457 13 18,901 18,775 2,583 North 'B' 7,457 65 153 1 0 0.0 3,843 34 11,452 11,300 3,996 Central 8,177 60 91 1 0 0.0 5,411 40 13,679 13,588 5,502 South 4,890 74 32 0 0 0.0 1,657 25 6,580 6,547 1,690 West 12,591 68 1,130 6 0 0.0 4,930 26 18,651 17,521 6,060 Wete 8,712 57 77 1 0 0.0 6,585 43 15,374 15,298 6,662 Micheweni 9,899 56 146 1 0 0.0 7,475 43 17,520 17,374 7,621 Chakechake 8,789 64 31 0 0 0.0 5,015 36 13,835 13,804 5,046 Mkoani 9,675 60 54 0 0 0.0 6,471 40 16,199 16,146 6,524 Total 86,509 65 1,840 1 0 0.0 43,844 33 132,193 130,353 45,684 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 66 2.3 TYPE OF AGRICULTURE HOUSEHOLD: Number of Agriculture Households By Type and Size of Holding, 2007/08 Agricultural Year Size of Holding (ha) Type of Agriculture Household Crops only Livestock only Crops and Livestock Total Number Percent Number Percent Number Percent Number Percent 0.01 - 0.50 31,359 78.8 1,756 4.4 6,656 16.7 39,771 100 0.51 - 1.00 28,467 66.7 29 0.1 14,173 33.2 42,670 100 1.01 - 1.50 15,922 57.2 29 0.1 11,893 42.7 27,843 100 1.51 - 2.00 5,286 49.7 0 0.0 5,354 50.3 10,640 100 2.01 - 2.50 3,109 47.4 26 0.4 3,424 52.2 6,559 100 2.51 - 3.00 908 46.1 0 0.0 1,062 53.9 1,970 100 3.01 - 3.50 494 52.4 0 0.0 450 47.6 944 100 3.51 - 4.00 279 58.8 0 0.0 195 41.2 474 100 4.01 -4.50 382 65.8 0 0.0 199 34.2 581 100 4.51 -5.00 61 27.9 0 0.0 159 72.1 220 100 Above 5 242 46.4 0 0.0 280 53.6 522 100 Total 86,509 65.4 1,840 1.4 43,844 33.2 132,193 100 2.4 ANIMAL CONTRIBUTION TO CROPS: Number of Households and Planted Area by Organic Fertiliser Use and District - SHORT RAINY SEASON District Organic Fertlizer Use % of Planted area using Organic Fertlizer Number of Households using Organic Fertlizer Planted Area Applied with Organic Fertlizer Number of Households NOT using Organic Fertlizer Planted Area NOT Applied with Organic Fertlizer Total Number of Households Planting in VULI Total Planted Area in VULI North 'A' 1,040 365 7,371 2,844 8,411 3,209 11.4 North 'B' 916 375 4,937 2,140 5,853 2,515 14.9 Central 2,006 884 5,411 2,416 7,417 3,300 26.8 South 1,056 202 3,136 826 4,192 1,028 19.6 West 1,507 484 4,333 1,250 5,840 1,734 27.9 Wete 102 36 1,973 696 2,076 733 5.0 Micheweni 701 305 4,117 1,022 4,818 1,327 23.0 Chakechake 93 35 946 302 1,039 336 10.3 Mkoani 80 11 1,794 613 1,874 624 1.7 Total 7,502 2,696 34,018 12,109 41,520 14,805 18.2 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 67 2.5 ANIMAL CONTRIBUTION TO CROPS: Number of Households and Planted Area by Organic Fertiliser Use and District - LONG RAINY SEASON Districts Organic Fertlizer Use % of Planted area using Organic Fertlizer Number of Households using Organic Fertlizer Planted Area Applied with Organic Fertlizer Number of Households NOT using Organic Fertlizer Planted Area NOT Applied with Organic Fertlizer Total Number of Households Planting in MASIKA Total Planted Area in MASIKA North 'A' 1,134 437 11,845 5,435 12,979 5,872 7.4 North 'B' 1,120 487 6,261 2,961 7,380 3,448 14.1 Central 1,246 721 5,259 2,600 6,505 3,321 21.7 South 227 30 910 150 1,137 179 16.5 West 1,162 388 6,782 2,436 7,944 2,825 13.7 Wete 743 283 13,248 5,804 13,991 6,087 4.6 Micheweni 701 304 12,848 4,959 13,549 5,263 5.8 Chakechake 178 96 10,991 5,055 11,169 5,152 1.9 Mkoani 295 179 13,415 5,327 13,709 5,506 3.2 Total 6,806 2,926 81,558 34,727 88,364 37,653 7.8 9.1.1 CATTLE PRODUCTION: Total Number of Households Rearing Cattle by District during 2007/08 Agriculture Year District Households rearing cattle Households not rearing cattle Total Agriculture households Total Number of Households Rearing Livestock Number % Number % North 'A' 1,796 9.5 17,106 90.5 18,901 2,583 North 'B' 3,181 27.8 8,271 72.2 11,452 3,996 Central 4,894 35.8 8,785 64.2 13,679 5,502 South 1,235 18.8 5,345 81.2 6,580 1,690 West 4,616 24.7 14,036 75.3 18,651 6,060 Wete 6,175 40.2 9,199 59.8 15,374 6,662 Micheweni 7,067 40.3 10,454 59.7 17,520 7,621 Chakechake 4,736 34.2 9,100 65.8 13,835 5,046 Mkoani 5,721 35.3 10,478 64.7 16,199 6,524 Total 39,420 29.8 92,773 70.2 132,193 45,684 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 68 9.1.2 CATTLE PRODUCTION: Number of Cattle by Type and District as of 1st October 2008 District Indigenous Improved Beef Improved Dairy Total Number of households Number of Cattle % Number of households Number of Cattle % Number of households Number of Cattle % Number of households Rearing cattle Number of Cattle % North 'A' 1,764 7,497 93.3 0 0 0.0 95 536 6.7 1,796 8,033 100 North 'B' 3,181 15,423 98.4 0 0 0.0 102 254 1.6 3,181 15,677 100 Central 4,681 25,625 92.6 0 0 0.0 730 2,037 7.4 4,894 27,662 100 South 1,235 4,793 99.7 0 0 0.0 16 16 0.3 1,235 4,809 100 West 4,490 19,342 91.5 0 0 0.0 659 1,790 8.5 4,616 21,132 100 Wete 5,945 20,935 95.4 0 0 0.0 487 999 4.6 6,175 21,934 100 Micheweni 7,067 23,185 99.0 0 0 0.0 58 234 1.0 7,067 23,419 100 Chakechake 4,612 15,021 94.0 0 0 0.0 248 961 6.0 4,736 15,982 100 Mkoani 5,721 16,922 99.7 0 0 0.0 27 54 0.3 5,721 16,976 100 Total 38,696 148,744 95.6 0 0 0.0 2,422 6,880 4.4 39,420 155,624 100 9.1.3 CATTLE PRODUCTION: Number of Households rearing cattle, Head of Cattle and Average Head per Household by Herd size During the 2007/08 Agricultural Year 9.1.4 CATTLE PRODUCTION: Total Number of Cattle by Cattle Types and Category, 2007/08 Agricultural Year Herd size Cattle Rearing Households % Herd of Cattle Average Per Household Cattle Types Indigeneous Improved Beef Improved Diary Total Cattle % 1 - 5 31,627 80.2 83,610 3 Castrated Bulls (Oxen) 3,906 0 151 4,057 2.1 6 - 10 6,001 15.2 43,716 7 Uncastrated Bulls 27,197 0 630 27,828 20.3 11 - 15 1,232 3.1 15,662 13 Cows 58,061 0 3,292 61,354 31.9 16 - 20 331 0.8 5,797 18 Steers 2,362 0 145 2,507 1.9 21 - 30 148 0.4 3,704 25 Heifers 24,972 0 900 25,873 17.2 31 - 40 51 0.1 1,736 34 Male Calves 14,910 0 837 15,747 12.2 41 - 50 30 0.1 1,398 46 Female Calves 17,334 0 923 18,258 14.3 Total 39,420 100.0 155,624 4 Tota 148,744 0 6,880 155,624 100.0 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 69 9.1.6 CATTLE PRODUCTION: Total Number of Improved Diary Cattle by Category of Cattle and District During the 2007/08 Agricultural Year District Cattle Type Castrated Bulls (Oxen) Uncastrated Bulls Cows Steers Heifers Male Calves Female Calves Total Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % North 'A' 95 16.7 0 0.0 252 33.3 0.0 0.0 32 16.7 95 16.7 63 16.7 536 100.0 North 'B' 0 0.0 0 0.0 127 50.0 0.0 0.0 51 16.7 51 16.7 25 16.7 254 100.0 Central 0 0.0 122 7.0 1,003 41.9 0.0 0.0 274 14.0 274 14.0 365 23.3 2,037 100.0 South 0 0.0 16 100.0 . 0.0 0.0 0.0 0 0.0 0 0.0 0 0.0 16 100.0 West 0 0.0 251 17.1 722 31.7 94.2 7.3 345 19.5 157 9.8 220 14.6 1,790 100.0 Wete 26 3.3 154 13.3 487 43.3 51.2 6.7 77 10.0 77 10.0 128 13.3 999 100.0 Micheweni 0 0.0 88 28.6 58 28.6 0.0 0.0 29 14.3 29 14.3 29 14.3 234 100.0 Chakechake 31 6.7 0 0.0 589 40.0 0.0 0.0 93 13.3 155 26.7 93 13.3 961 100.0 Mkoani 0 0.0 0 0.0 54 100.0 0.0 0.0 0 0.0 0 0.0 0 0.0 54 100.0 Total 151 2.0 630 11.1 3,292 38.5 145.4 3.3 900 14.9 837 13.5 923 16.8 6,880 100.0 9.1.5 CATTLE PRODUCTION: Total Number of Indigenous Cattle by Category of Cattle and District During the 2007/08 Agricultural Year District Cattle Type Castrated Bulls (Oxen) Uncastrated Bulls Cows Steers Heifers Male Calves Female Calves Total Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % Total Cattle % North 'A' 95 2.48 1,071 22.31 3,654 36.36 . .00 819 10.74 630 10.74 1,229 17.36 7,497 100.00 North 'B' 865 1.85 2,341 19.44 5,599 30.86 433 3.09 1,705 10.19 1,858 15.12 2,621 19.44 15,423 100.00 Central 578 2.61 4,225 21.67 10,518 28.46 365 1.31 4,560 17.75 2,584 14.10 2,797 14.10 25,625 100.00 South 130 2.31 942 22.54 2,193 36.42 114 2.89 585 13.29 390 9.83 439 12.72 4,793 100.00 West 502 2.29 4,333 25.79 7,096 28.37 251 2.01 2,795 15.19 2,010 11.46 2,355 14.90 19,342 100.00 Wete 128 .40 3,818 18.40 8,661 34.60 256 1.80 3,459 19.60 2,281 12.00 2,332 13.20 20,935 100.00 Micheweni 526 2.44 4,380 19.92 8,059 30.45 175 .94 4,847 19.17 2,453 12.78 2,745 14.29 23,185 100.00 Chakechake 806 3.10 2,480 15.48 5,953 36.46 527 4.13 2,124 15.05 1,581 12.73 1,550 13.07 15,021 100.00 Mkoani 277 1.57 3,606 24.23 6,328 30.77 241 1.49 4,079 24.48 1,125 7.69 1,267 9.76 16,922 100.00 Total 3,906 2.63 27,197 18.28 58,061 39.03 2,362 1.59 24,972 16.79 14,910 10.02 17,334 11.65 148,744 100.00 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 70 9.1.7 CATTLE PRODUCTION: Total Number Households rearing Cattle and Method of Cattle Identification by District during, 2007/08 Agricultural Year District Branding Cattle Clan Ear notching Colour Earings Others Total Number % Number % Number % Number % Number % Number % Number % North 'A' 63 3.4 284 15.3 95 5.1 1,260 67.8 0 0.0 158 8.5 1,859 100.0 North 'B' 127 4.0 534 16.8 102 3.2 2,061 64.8 25 0.8 331 10.4 3,181 100.0 Central 182 3.7 547 11.0 61 1.2 3,526 71.2 152 3.1 486 9.8 4,955 100.0 South 16 1.3 162 13.0 65 5.2 959 76.6 16 1.3 32 2.6 1,251 100.0 West 63 1.4 345 7.4 31 0.7 3,171 68.2 283 6.1 754 16.2 4,647 100.0 Wete 128 2.1 512 8.3 128 2.1 5,099 82.2 0 0.0 333 5.4 6,201 100.0 Micheweni 292 4.1 321 4.5 58 0.8 5,928 83.5 0 0.0 496 7.0 7,096 100.0 Chakechake 0 0.0 248 5.2 0 0.0 3,589 75.8 0 0.0 899 19.0 4,736 100.0 Mkoani 27 0.5 357 6.2 80 1.4 4,962 86.7 0 0.0 295 5.1 5,721 100.0 Total 899 2.3 3,312 8.4 620 1.6 30,556 77.1 476 1.2 3,784 9.5 39,646 100.0 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 71 9.2.1 CATTLE PRODUCTION: Number of Milked Cows by Category of Cattle, Season and District, During the 2007/08 Agricultural Year District Wet Season Dry Season Improved Breed Indigenous Total Improved Breed Indigenous Total North 'A' 126 3,339 3,465 189 2,835 3,024 North 'B' 153 4,657 4,810 153 4,326 4,479 Central 942 6,748 7,691 882 4,104 4,985 South 0 959 959 49 650 699 West 816 5,495 6,311 659 4,961 5,621 Wete 333 6,637 6,970 487 5,509 5,996 Micheweni 117 5,607 5,723 88 4,847 4,935 Chakechake 434 4,589 5,023 279 3,658 3,937 Mkoani . 3,766 3,766 . 2,963 2,963 Total 2,921 41,796 44,718 2,785 33,854 36,639 9.2.2 CATTLE PRODUCTION: Average milk production per cow per day, by Category of Cow, Season and District, During the 2007/08 Agricultural Year District Wet Season Dry Season Improved Breed Indigenous Total Improved Breed Indigenous Total Mean (ltr) Mean (lts) Mean (lts) Mean (lts) Mean (lts) Mean (lts) North 'A' 12 3 3 12 2 2 North 'B' 6 3 3 6 2 3 Central 6 2 3 5 2 2 South . 2 2 2 1 1 West 10 3 4 10 3 4 Wete 6 2 2 6 2 2 Micheweni 4 2 2 4 2 2 Chakechake 6 2 2 7 2 2 Mkoani 0 2 2 0 2 2 Total 7 2 2.5 7 2 2.3 9.2.3 CATTLE PRODUCTION: Average Number of days for Cows on Milked, by Category of Cattle, Season and District, During the 2007/08 Agricultural Year District Wet Season Dry Season Improved Breed Indigenous Total Improved Breed Indigenous Total Mean Mean Mean Mean Mean Mean North 'A' 45 99 98 45 98 96 North 'B' 163 115 117 163 102 104 Central 154 125 130 120 109 112 South . 86 86 53 89 86 West 190 136 145 186 130 137 Wete 131 120 121 140 121 123 Micheweni 69 93 93 54 88 87 Chakechake 164 106 111 151 113 116 Mkoani 0 117 117 0 105 105 Total 155 114 117 137 108 111 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 72 9.2.4 CATTLE PRODUCTION: Average Cattle Milk price (Tshs/litre) per season by category of cow and District, During the 2007/08 Agricultural Year District Wet Season Dry Season Improved Breed Indigenous Total Improved Breed Indigenous Total Mean Mean Mean Mean Mean Mean North 'A' 900 553 570 800 565 572 North 'B' 450 449 449 450 468 467 Central 435 453 450 445 465 461 South . 491 491 750 492 512 West 541 494 502 583 508 518 Wete 491 508 507 509 496 497 Micheweni 575 455 459 633 484 488 Chakechake 557 445 456 543 482 488 Mkoani 60 506 501 0 533 533 Total 507 479 481 522 495 497 9.3.1 GOAT PRODUCTION: Number of Agriculture Households Rearing Goats by District during the 2007/08 Agricultural Year District Raising goats Not raising goats Total Total Number of Households Rearing Livestock No of households % No of households % North 'A' 1,386 7.3 17,515 92.7 18,901 2,583 North 'B' 1,120 9.8 10,333 90.2 11,452 3,996 Central 2,280 16.7 11,399 83.3 13,679 5,502 South 845 12.8 5,735 87.2 6,580 1,690 West 1,664 8.9 16,987 91.1 18,651 6,060 Wete 769 5.0 14,606 95.0 15,374 6,662 Micheweni 1,810 10.3 15,710 89.7 17,520 7,621 Chakechake 1,341 9.7 12,494 90.3 13,835 5,046 Mkoani 1,892 11.7 14,307 88.3 16,199 6,524 Total 13,107 9.9 119,086 90.1 132,193 45,684 9.3.2 GOAT PRODUCTION: Number of Goats by Type and District as of 1st October 2008 District Indigenous Improved for Meat Improved Dairy Total Number of households Number of Goats % Number of househol ds Number of Goats % Number of househol ds Numbe r of Goats % Households Rearing goats Number of Goats North 'A' 1,386 6,269 76 32 63 1 32 1,890 23.0 1,386 8,222 North 'B' 1,120 6,286 100 0 0 0 0 0 0.0 1,120 6,286 Central 2,189 8,694 53 0 0 0 182 7,721 47.0 2,280 16,415 South 845 4,500 100 0 0 0 0 0 0.0 845 4,500 West 1,633 10,079 84 0 0 0 63 1,947 16.2 1,664 12,026 Wete 717 1,973 90 0 0 0 51 231 10.5 769 2,204 Micheweni 1,810 6,775 100 0 0 0 0 0 0.0 1,810 6,775 Chakechake 1,225 4,627 98 0 0 0 116 116 2.5 1,341 4,744 Mkoani 1,892 7,801 100 0 0 0 0 0 0.0 1,892 7,801 Total 12,817 57,004 83 32 63 0 444 11,905 17.3 13,107 68,972 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 73 9.3.3. Goat PRODUCTION: Number of Households rearing Goat, Head of Goat and Average Head per Household by Herd size During the 2007/08 Agricultural Year District Goat Rearing Households % Heard of Goat Average Per Houseold 1 - 4 8,372 63.88 20,734 2.48 5 - 9 3,486 26.60 21,131 6.06 10 - 14 826 6.30 9,369 11.34 15 - 19 163 1.24 2,536 15.58 20 - 24 58 .44 1,289 22.16 40+ 202 1.54 13,914 68.97 Total 13,107 100.00 68,972 5.26 9.3.4 GOAT PRODUCTION: Total Number of Goats by Category and Type of Goat as of 1st October 2008 Category Indigenous Improved Meat Improved Dairy Total Number % Number % Number % Number % Billy Goat 8,294 89 63 1 938 10 9,295 1,909 Castrated Goat 1,294 100 0 0 0 0 1,294 266 She Goat 31,698 85 0 0 5,677 15 37,375 7,677 Male Kid 7,692 80 0 0 1,880 20 9,572 1,966 She Kid 8,025 70 0 0 3,410 30 11,435 2,349 Total 57,004 83 63 0 11,905 17 68,972 14,167 9.3.5 GOAT PRODUCTION: Total Number of Indigenous Goat by Category and District as of 1st October 2008 District Goat Type Billy Goat Castrated Goat She Goat Male Kid She Kid Total Total Goat % Total Goat % Total Goat % Total Goat % Total Goat % Total Goat % North 'A' 756 12.1 126 2.0 3,654 58.3 882 14.1 851 13.6 6,269 100.0 North 'B' 1,043 16.6 127 2.0 3,283 52.2 738 11.7 1,094 17.4 6,286 100.0 Central 1,125 12.9 152 1.7 5,076 58.4 1,155 13.3 1,186 13.6 8,694 100.0 South 764 17.0 130 2.9 2,128 47.3 731 16.2 747 16.6 4,500 100.0 West 1,413 14.0 188 1.9 5,495 54.5 1,319 13.1 1,664 16.5 10,079 100.0 Wete 487 24.7 26 1.3 1,076 54.5 154 7.8 231 11.7 1,973 100.0 Micheweni 964 14.2 234 3.4 3,796 56.0 1,110 16.4 672 9.9 6,775 100.0 Chakechake 806 17.4 124 2.7 2,922 63.1 372 8.0 403 8.7 4,627 100.0 Mkoani 937 12.0 187 2.4 4,266 54.7 1,232 15.8 1,178 15.1 7,801 100.0 Total 8,294 14.6 1,294 2.3 31,698 55.6 7,692 13.5 8,025 14.1 57,004 100.0 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 74 9.3.7 GOAT PRODUCTION: Number of Improved Dairy Goats by Category and District as of 1st October 2008 District Goat Type Billy Goat Castrated Goat She Goat Male Kid She Kid Total Number % Number % Number % Number % Number % Number % North 'A' 0 0 0 0 0 0 0 0 1,890 100.00 1,890 100.00 North 'B' 0 0 0 0 0 0 0 0 0 0 0 0 Central 912 11.81 0 0 3,435 44.49 1,854 24.02 1,520 19.69 7,721 100.00 South 0 0 0 0 0 0 0 0 0 0 0 0 West 0 0 0 0 1,947 100.00 0 0 0 0 1,947 100.00 Wete 26 11.11 0 0 179 77.78 26 11.11 0 0 231 100.00 Micheweni 0 0 0 0 0 0 0 0 0 0 0 0 Chakechake 0 0 0 0 116 100.00 0 0 0 0 116 100.00 Mkoani 0 0 0 0 0 0 0 0 0 0 0 0 Total 938 7.88 0 0 5,677 47.69 1,880 15.79 3,410 28.64 11,905 100.00 9.3.6 GOAT PRODUCTION: Number of Improved Goats for Meat by Category and District as of 1st October 2008 District Goat Type Billy Goat Castrated Goat She Goat Male Kid She Kid Total Total Goat % Total Goat % Total Goat % Total Goat % Total Goat % Total Goat % North 'A' 63 100.00 0 0 0 0 0 0 0 0 63 100.00 North 'B' 0 0 0 0 0 0 0 0 0 0 0 0 Central 0 0 0 0 0 0 0 0 0 0 0 0 South 0 0 0 0 0 0 0 0 0 0 0 0 West 0 0 0 0 0 0 0 0 0 0 0 0 Wete 0 0 0 0 0 0 0 0 0 0 0 0 Micheweni 0 0 0 0 0 0 0 0 0 0 0 0 Chakechake 0 0 0 0 0 0 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 0 0 0 0 0 0 Total 63 100.00 0 0 0 0 0 0 0 0 63 100.00 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 75 9.4.2 SHEEP PRODUCTION: Number of Household Rearing and number of Sheep by Type and District as of 1st October 2008 District Total Number of house hold % Number of Indigenous % Number of Improved for Mutton % Total Sheep North 'A' 32 0.2 32 100.0 0 0.0 32 North 'B' 25 0.2 51 100.0 0 0.0 51 Central 61 0.4 122 100.0 0 0.0 122 South 0 0.0 0 100.0 0 0.0 0 West 63 0.3 283 100.0 0 0.0 283 Wete 0 0.0 0 100.0 0 0.0 0 Micheweni 29 0.2 88 100.0 0 0.0 88 Chakechake 0 0.0 0 100.0 0 0.0 0 Mkoani 0 0.0 0 100.0 0 0.0 0 Total 210 0.2 574 100.0 0 0.0 574 9.3.8 Goat PRODUCTION: Number of Milked Goat by Category of Goat, Season type and District, During the 2007/08 Agricultural Year District Number of Milked goat Average milk production per goat per day Average number of days goats are milked Average price per litre per season Wet Season Dry Season Total Wet Season Dry Season Total Wet Season Dry Season Total Wet Season Dry Season Total North 'A' 0 0 0 0 0 0 0 0 0 0 0 0 North 'B' 0 0 0 0 0 0 0 0 0 0 0 0 Central 304 182 486 .7 .6 .7 78 72 75 960 960 960 South 0 0 0 0 0 0 0 0 0 0 0 0 West 126 63 188 2.0 2.0 2.0 75 60 70 1000 1000 1000 Wete 102 102 205 1.8 1.3 1.6 70 70 70 667 667 667 Micheweni 0 0 0 0 0 0 0 0 0 1000 0 1000 Chakechake 0 0 0 0 0 0 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 0 0 0 1100 1000 1050 Total 532 348 880 1.3 1.0 1.1 75 70 73 917 890 904 9.4.1 SHEEP PRODUCTION: Total Number Households Rearing Sheep by District during, 2007/08 Agricultural Year District Households rearing Sheep Households NOT rearing Sheep Total Number % Number % Number % North 'A' 32 0.2 18,870 99.8 18,901 100.0 North 'B' 25 0.2 11,427 99.8 11,452 100.0 Central 61 0.4 13,618 99.6 13,679 100.0 South 0 0.0 6,580 100.0 6,580 100.0 West 63 0.3 18,588 99.7 18,651 100.0 Wete 0 0.0 15,374 100.0 15,374 100.0 Micheweni 29 0.2 17,491 99.8 17,520 100.0 Chakechake 0 0.0 13,835 100.0 13,835 100.0 Mkoani 0 0.0 16,199 100.0 16,199 100.0 Total 210 0.2 131,983 99.8 132,193 100.0 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 76 9.4.3 SHEEP PRODUCTION: Total Number of Indigenous Sheep by Category of Sheep and District as of 1st October 2007/08 Agriculture year District Number of Indigenous Total Ram Castrated Sheep She Sheep Male Lamb She Lamb North 'A' 0 32 0 0 0 32 North 'B' 0 0 51 0 0 51 Central 30 61 30 0 0 122 South 0 0 0 0 0 0 West 31 0 157 0 94 283 Wete 0 0 0 0 0 0 Micheweni 58 0 29 0 0 88 Chakechake 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 Total 120 92 267 0 94 574 9.4.4 SHEEP PRODUCTION: Number of Households rearing Sheep, Head of Sheep and Average Head per Household by Herd size During the 2007/08 Agricultural Year Herd size Sheep Rearing Households % Herd of sheep Average Per Houseold 1 - 4 178 85.03 386 2 5 - 9 31 14.97 188 6 Total 210 100.00 574 3 9.5.1 PIG PRODUCTION: Number of Households Raising Pigs by Districts during 2007/08 Agriculture Year District Households Rearing Pigs Not Rearing pigs Total Number % Number % Number % North 'A' 0 0.0 18,901 100.0 18,901 100.0 North 'B' 0 0.0 11,452 100.0 11,452 100.0 Central 122 0.9 13,557 99.1 13,679 100.0 South 0 0.0 6,580 100.0 6,580 100.0 West 31 0.2 18,620 99.8 18,651 100.0 Wete 0 0.0 15,374 100.0 15,374 100.0 Micheweni 0 0.0 17,520 100.0 17,520 100.0 Chakechake 0 0.0 13,835 100.0 13,835 100.0 Mkoani 0 0.0 16,199 100.0 16,199 100.0 Total 153 0.1 132,040 99.9 132,193 100.0 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 77 9.5.2 PIG PRODUCTION: Number of Households Rearing Pigs, Head of Pigs and Average Head per Household by Herd Size as of 1st October 2008 Herd Size Pig rearing households Head of pigs Average per household Number % Number % 5 - 9 61 40 395 13.1 7 15 - 19 30 20 578 19.2 19 30 - 39 62 40 2,042 67.7 33 Total 153 100 3,015 100.0 20 9.5.3 PIG PRODUCTION: Total Number of Pigs by Type of Pigs and District as of 1st October 2008 Diatrict Pig Type Boar Castrated Male Sow / Gilt Male Piglet She Piglet Total North 'A' 0 0 0 0 0 0 North 'B' 0 0 0 0 0 0 Central 122 182 608 638 334 1,885 South 0 0 0 0 0 0 West 0 0 126 0 1,005 1,130 Wete 0 0 0 0 0 0 Micheweni 0 0 0 0 0 0 Chakechake 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 Total 122 182 734 638 1,339 3,015 9.6.1 CHICKEN PRODUCTION: Number of Chicken by Type and District as of 1st October 2008 Diatrict Indigineous chicken Layers Broilers Total Number of Households Number of Indigineous Chicken % Number of Households Number of Layers % Number of Households Number of Broilers % Households Rearing Chicken Number of Chicken North 'A' 7,718 76,455 92 63 6,584 8 32 189 0.2 7,812 83,228 North 'B' 7,355 100,476 83 254 15,703 13 25 5,090 4.2 7,635 121,269 Central 7,478 99,918 97 30 3,040 3 30 213 0.2 7,539 103,171 South 3,428 48,025 93 65 3,867 7 0 0 0.0 3,493 51,892 West 10,770 139,445 61 471 80,508 35 63 9,420 4.1 11,304 229,373 Wete 11,403 135,501 91 179 13,760 9 0 0 0.0 11,582 149,261 Micheweni 10,746 97,851 98 117 934 1 117 1,548 1.5 10,979 100,333 Chakechake 9,100 101,893 95 93 5,023 5 0 0 0.0 9,193 106,915 Mkoani 10,425 132,905 100 107 616 0 0 0 0.0 10,532 133,521 Total 78,422 932,469 86 1,380 130,034 12 267 16,459 1.5 80,069 1,078,962 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 78 9.6.2 CHICKEN PRODUCTION : Number of Households Keeping Chickens and Average Number of Chickens per Household by Flock Size as of 1st October 2008 Flock Size Indigineous chicken Layers Broilers Number of Household s Number of Indigenous Chicken % Numb er of Chick en Per House hold Number of Househol ds Number of Layers % Number of Chicken Per Househol d Num ber of Hous ehold s Number of Broilers % Number of Chicken Per Househol d 1-49 76,731 795,432 95 10 721 7,345 1 10 179 1,949 0.2 11 50-99 1,306 76,320 85 58 145 9,925 11 68 0 0 0.0 0 100-299 359 50,537 39 141 394 59,600 46 151 88 14,510 11. 2 164 300-499 25 10,180 27 400 88 28,045 73 318 0 0 0.0 0 700+ 0 0 0 0 31 25,120 100 800 0 0 0.0 0 Total 78,422 932,469 86 12 1,380 130,034 12 94 267 16,459 1.5 62 9.6.3 CHICKEN PRODUCTION: Number of Other Livestock by Type of livestock by District as of 1st October 2008 District Ducks Guine pigs Turkeys Rabbits Donkeys Horses Dogs North 'A' 6,332 0 0 0 63 0 189 North 'B' 4,556 331 305 0 0 0 585 Central 2,097 213 122 0 30 0 547 South 1,803 81 244 97 0 0 162 West 16,077 0 157 722 0 0 1,758 Wete 1,589 0 0 256 51 0 410 Micheweni 555 175 0 0 0 0 175 Chakechake 922 23 0 186 155 0 279 Mkoani 348 0 54 0 54 0 107 Total 34,279 823 881 1,262 353 0 4,214 9.6.4CHICKEN PRODUCTION: Number of Households Keeping Other Livestock and Average Number per Household by Flock Size as of 1st October 2008 Flock Size Ducks Guine pigs Turkeys Numbe r of Househ olds Number of Ducks % Number of Duck Per Househol d Number of Household s Number of Guine pigs % Number of Guine pigs Per Househol d Number of Househol ds Numb er of Turkey s % Number of Turkeys Per Househol d 1-49 3,233 26,244 3 8 150 823 0.1 5 229 881 0.1 4 50-99 57 3,309 4 58 0 0 0.0 0 0 0 0.0 0 100-299 32 4,725 4 150 0 0 0.0 0 0 0 0.0 0 300-499 0 0 0 0 0 0 0.0 0 0 0 0.0 0 700+ 0 0 0 0 0 0 0.0 0 0 0 0.0 0 Total 3,321 34,279 3 10 150 823 0.1 5 229 881 0.1 4 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 79 9.65 : THER LIVESTOCK : Total Number of Other Livestock by Type as of 1st October 2008 Type Chicken Others Number % Type Number Indigenous Chicken 932,469 86.422751 Ducks 34,279 Layer 130,034 12.05178 Guine pigs 823 Broiler 16,459 1.5254694 Turkeys 881 0 Rabbits 1,262 0 Donkeys 353 0 Horses 0 0 Dogs 4,214 TOTAL 1,078,962 100 155792.842 9.7.1: PEST AND PARASITES: Number of Livestock Rearing households deworming Livestock by District during 2007/08 Agriculture Year District Deworming Livestock Not Deworm Livestock Total Number % Number % Number of Livestock Rearing households % North 'A' 2,583 29 6,426 71 9,010 100 North 'B' 2,672 31 5,828 69 8,500 100 Central 4,742 52 4,408 48 9,150 100 South 1,121 27 2,989 73 4,110 100 West 5,778 45 6,971 55 12,748 100 Wete 2,998 24 9,686 76 12,684 100 Micheweni 3,037 24 9,870 76 12,907 100 Chakechake 2,907 28 7,557 72 10,464 100 Mkoani 2,276 19 9,532 81 11,808 100 Total 28,113 30 63,267 70 91,380 100 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 80 9.7.2: PEST AND PARASITES: Number of Livestock Rearing households that dewormed Livestock by type of livestock and District, 2007/08 Agricultural Year District Cattles Goats/sheeps Dewormed Pig Dewormed Chicken Household s that dewormed Household s that DID NOT deworm Not Applicabl e Total Household s that dewormed Household s that DID NOT deworm Not Applicabl e Total Household s that dewormed Household s that DID NOT deworm Not Applicabl e Total Household s that dewormed Household s that DID NOT deworm Not Applicabl e Total North 'A' 977 378 1,260 2,615 347 410 1,890 2,646 0 0 2,615 2,615 1,481 945 567 2,993 North 'B' 1,400 153 1,222 2,774 534 102 2,112 2,749 0 0 2,749 2,749 1,222 942 611 2,774 Central 3,313 334 1,186 4,833 912 912 3,009 4,833 91 31 4,529 4,651 1,885 2,067 1,094 5,046 South 617 130 504 1,251 341 211 666 1,218 0 0 1,170 1,170 487 699 260 1,446 West 2,857 471 2,638 5,966 691 502 4,773 5,966 31 0 5,621 5,652 3,360 1,915 1,068 6,343 Wete 2,076 846 589 3,511 231 359 2,486 3,075 0 0 2,998 2,998 1,281 2,332 333 3,946 Micheweni 2,278 701 584 3,562 409 555 2,394 3,358 0 0 3,037 3,037 934 2,570 526 4,030 Chakechak e 1,907 248 783 2,938 457 186 2,325 2,969 0 0 2,938 2,938 806 1,635 496 2,938 Mkoani 1,660 241 509 2,410 187 402 1,740 2,329 0 0 2,276 2,276 1,151 1,205 295 2,651 Total 17,084 3,501 9,273 29,85 9 4,109 3,638 21,396 29,14 3 122 31 27,120 28,08 4 12,607 14,310 5,249 32,16 6 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 81 9.7.3 PEST AND PARASITES: Number of Livestock Rearing Households Normally Encountering Tick Problems by District during 2007/08 Agriculture Year District Tick Problem No Tick Problem Not Applicable Total Number % Number % Number % Number % North 'A' 1,418 5 16 1,670 18 6,017 66 9,104 100 North 'B' 2,672 9 32 1,196 14 4,606 54 8,475 100 Central 4,377 15 48 1,581 17 3,192 35 9,150 100 South 1,024 3 25 991 24 2,096 51 4,110 100 West 3,611 12 27 2,418 18 7,316 55 13,345 100 Wete 4,254 14 33 2,357 18 6,150 48 12,761 100 Micheweni 4,585 15 36 3,329 26 4,993 39 12,907 100 Chakechake 4,201 14 39 977 9 5,472 51 10,650 100 Mkoani 3,981 13 34 2,651 22 5,177 44 11,808 100 Total 30,121 100 33 17,169 19 45,019 49 92,309 100 9.7.4 PEST AND PARASITES: Number of Livestock Rearing Households by Method of Tick Control and District during 2007/08 Agriculture Year District Dipping Spraying Smearing None Other Total Number % Number % Number % Number % Number % Number % North 'A' 410 4 819 9 410 4 7,371 81 95 1 9,104 100 North 'B' 254 3 1,451 17 585 7 6,108 72 76 1 8,475 100 Central 426 5 2,128 23 1,763 19 4,742 52 91 1 9,150 100 South 65 2 845 21 244 6 2,941 72 16 0 4,110 100 West 408 3 2,512 19 1,978 15 8,289 62 157 1 13,345 100 Wete 820 6 2,101 16 974 8 8,430 66 436 3 12,761 100 Micheweni 759 6 2,307 18 701 5 8,760 68 380 3 12,907 100 Chakechake 186 2 2,821 26 488 5 6,759 63 395 4 10,650 100 Mkoani 696 6 2,321 20 464 4 7,979 68 348 3 11,808 100 Total 4,024 4 17,304 19 7,607 8 61,380 66 1,994 2 92,309 100 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 82 9.7.5: PEST AND PARASITES: Number of Livestock Rearing Households normally Encountering Newcastle Disease Problems by District during 2007/08 Agriculture Year District Households Encoutering Newcastle Disease problems Households NOT Encoutering Newcastle Disease problems Not Applicable Total Number % Number % Number % Number % North 'A' 4,442 49 3,623 40 1,040 11 9,104 100 North 'B' 5,650 67 1,832 22 993 12 8,475 100 Central 5,289 58 2,432 27 1,429 16 9,150 100 South 1,950 47 1,608 39 552 13 4,110 100 West 8,446 63 3,266 24 1,633 12 13,345 100 Wete 8,072 63 3,511 28 1,179 9 12,761 100 Micheweni 6,220 48 4,497 35 2,190 17 12,907 100 Chakechake 7,875 74 1,907 18 868 8 10,650 100 Mkoani 5,587 47 4,936 42 1,285 11 11,808 100 Total 53,530 58 27,611 30 11,168 12 92,309 100 9.7.6: PEST AND PARASITES: Number of Livestock Rearing Households by Method of Newcastle Disease Control by District during 2007/08 Agriculture Year District Vaccination Local Herbs None Total Number % Number % Number % Number % North 'A' 851 9 1,953 21 6,300 69 9,104 100 North 'B' 611 7 993 12 6,871 81 8,475 100 Central 1,186 13 1,733 19 6,232 68 9,150 100 South 276 7 877 21 2,957 72 4,110 100 West 2,386 18 2,512 19 8,446 63 13,345 100 Wete 948 7 1,589 12 10,224 80 12,761 100 Micheweni 1,372 11 1,606 12 9,928 77 12,907 100 Chakechake 760 7 992 9 8,898 84 10,650 100 Mkoani 1,205 10 1,205 10 9,398 80 11,808 100 Total 9,594 10 13,459 15 69,255 75 92,309 100 9.7.7 PEST AND PARASITES: Number of Livestock Rearing Households normally Encountering Fowl Typhoid Disease Problems by District during 2007/08 Agriculture Year District Households Encoutering Fowl Typhoid Disease problems Households NOT Encoutering Fowl Typhoid Disease problems Not Applicable Total Number % Number % Number % Number % North 'A' 1,103 12 6,395 70 1,607 18 9,104 100 North 'B' 1,807 21 5,523 65 1,145 14 8,475 100 Central 1,307 14 6,414 70 1,429 16 9,150 100 South 504 12 3,022 74 585 14 4,110 100 West 4,019 30 7,473 56 1,853 14 13,345 100 Wete 3,895 31 7,534 59 1,332 10 12,761 100 Micheweni 3,037 24 7,446 58 2,424 19 12,907 100 Chakechake 2,473 23 7,092 67 1,085 10 10,650 100 Mkoani 1,892 16 8,497 72 1,419 12 11,808 100 Total 20,036 22 59,395 64 12,878 14 92,309 100 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 83 9.7.8 PEST AND PARASITES: Number of Livestock Rearing Households by Method of Fowl Typhoid Disease Control by District during 2007/08 Agriculture Year District Vaccination Local Herbs None Total Number % Number % Number % Number % North 'A' 347 4 945 10 7,812 86 9,104 100 North 'B' 153 2 484 6 7,839 92 8,475 100 Central 274 3 699 8 8,207 89 9,180 100 South 81 2 390 9 3,639 89 4,110 100 West 848 6 1,664 12 10,833 81 13,345 100 Wete 461 4 1,461 11 10,839 85 12,761 100 Micheweni 292 2 1,431 11 11,184 87 12,907 100 Chakechake 248 2 496 5 9,906 93 10,650 100 Mkoani 268 2 991 8 10,550 89 11,808 100 Total 2,971 3 8,560 9 80,809 88 92,339 100 9.7.9: PEST AND PARASITES: Number of Livestock Rearing Households normally Encountering Foot and Mouth Disease Problems by District during 2007/08 Agriculture Year District Yes No Not Applicable Total Number % Number % Number % Number % North 'A' 221 5 3 1,764 20 6,804 77 8,789 100 North 'B' 560 12 7 2,570 30 5,344 63 8,475 100 Central 1,216 27 14 4,043 45 3,709 41 8,967 100 South 227 5 6 1,218 31 2,453 63 3,899 100 West 1,507 33 12 3,360 26 8,101 62 12,968 100 Wete 179 4 1 6,688 54 5,407 44 12,274 100 Micheweni 234 5 2 7,154 58 4,964 40 12,352 100 Chakechake 155 3 1 4,705 44 5,790 54 10,650 100 Mkoani 214 5 2 5,766 50 5,560 48 11,540 100 Total 4,513 100 5 37,268 41 48,133 54 89,914 100 9.7.10: PEST AND PARASITES: Number of Livestock Rearing Households normally Encountering Lympyskin Disease Problems by District during 2007/08 Agriculture Year District Yes No Not Applicable Total Number % Number % Number % Number % North 'A' 284 4 3 1,827 21 6,678 76 8,789 100 North 'B' 840 11 10 2,341 28 5,294 62 8,475 100 Central 1,763 23 20 3,617 40 3,648 40 9,028 100 South 227 3 6 1,251 32 2,421 62 3,899 100 West 1,664 21 13 3,077 24 8,227 63 12,968 100 Wete 1,127 14 9 5,714 47 5,432 44 12,274 100 Micheweni 701 9 6 6,658 54 4,964 40 12,323 100 Chakechake 558 7 5 4,147 39 5,976 56 10,681 100 Mkoani 669 9 6 5,284 46 5,587 48 11,540 100 Total 7,834 100 9 33,916 38 48,227 54 89,977 100 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 84 9.8.1 LIVESTOCK EXTENSION: Number of households Receiving Extension advice by District during the 2007/08 Agriculture year District Receiving Livestock services Not Receiving Livestock Extension services Livestock keeper Number % Number % North 'A' 2,268 9.7 6,741 75 9,010 North 'B' 3,258 14.0 5,243 62 8,500 Central 3,921 16.8 5,228 57 9,150 South 1,072 4.6 3,038 74 4,110 West 5,212 22.3 7,536 59 12,748 Wete 2,306 9.9 10,378 82 12,684 Micheweni 1,606 6.9 11,301 88 12,907 Chakechake 1,318 5.6 9,146 87 10,464 Mkoani 2,374 10.2 9,434 80 11,808 Total 23,336 100.0 68,045 74 91,380 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 85 9.8.2 LIVESTOCK EXTENSION: Number of Households receiving Livestock advice (overall) By Source of Extension and District during the 2007/08 agriculture year District Source of Livestock Extension Number of Household Receiving Extension Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Number % Number % Number % Number % Number % Number % North 'A' 1,260 55.6 347 15.3 32 1.4 315 13.9 252 11.1 504 22.2 2,268 North 'B' 2,138 65.6 127 3.9 382 11.7 458 14.1 1,171 35.9 1,171 35.9 3,258 Central 2,097 53.5 942 24.0 274 7.0 1,064 27.1 547 14.0 882 22.5 3,921 South 796 74.2 81 7.6 65 6.1 97 9.1 97 9.1 390 36.4 1,072 West 2,041 39.2 1,444 27.7 314 6.0 1,036 19.9 1,413 27.1 1,444 27.7 5,212 Wete 1,666 72.2 359 15.6 0 0.0 0 0.0 307 13.3 154 6.7 2,306 Micheweni 1,168 72.7 58 3.6 0 0.0 204 12.7 29 1.8 350 21.8 1,606 Chakechake 791 60.0 217 16.5 0 0.0 0 0.0 302 22.9 178 13.5 1,318 Mkoani 1,830 77.1 116 4.9 0 0.0 89 3.8 375 15.8 161 6.8 2,374 Total 13,786 59.1 3,692 15.8 1,066 4.6 3,264 14.0 4,494 19.3 5,234 22.4 23,336 9.8.3 LIVESTOCK EXTENSION: Number of Agriculture Households Receiving Advice on Feeds and Proper Feeding by Source and District During 2007/08griculture Year District Source of Livestock Extension Number of Household Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Number % Number % Number % Number % Number % Number % North 'A' 599 59.375 63 6.25 0 0 95 9.375 63 6.25 189 18.75 1,008 North 'B' 1,171 63.889 0 0 76 4.1667 102 5.556 153 8.33333 331 18.0556 1,832 Central 608 40 334 22 0 0 213 14 182 12 182 12 1,520 South 114 70 32 20 0 0 16 10 0 0 0 0 162 West 973 31.959 283 9.2784 94 3.0928 377 12.37 722 23.7113 597 19.5876 3,046 Wete 436 73.913 102 17.391 0 0 0 0 51 8.69565 0 0 589 Micheweni 175 54.545 29 9.0909 0 0 88 27.27 0 0 29 9.09091 321 Chakechake 109 31.818 124 36.364 0 0 0 0 23 6.81818 85 25 341 Mkoani 669 84.27 36 4.4944 0 0 36 4.494 54 6.74157 0 0 794 Total 4,853 50.48 1,004 10.44 171 1.774 925 9.625 1,248 12.984 1,413 14.7 9,615 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 86 9.8.4 LIVESTOCK EXTENSION: Number of households receiving Extension Advice on ProperLivestock Housing by District during the 2007/08 Agriculture Year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 630 64.5 95 9.7 32 3.2 95 9.7 63 6.5 63 6.5 0 0.0 977 North 'B' 942 46.8 25 1.3 127 6.3 229 11.4 280 13.9 382 19.0 25 1.3 2,011 Central 669 36.7 334 18.3 91 5.0 243 13.3 213 11.7 274 15.0 0 0.0 1,824 South 179 84.6 16 7.7 0 0.0 16 7.7 0 0.0 0 0.0 0 0.0 211 West 848 33.3 345 13.6 63 2.5 440 17.3 565 22.2 283 11.1 0 0.0 2,543 Wete 461 64.3 231 32.1 0 0.0 0 0.0 26 3.6 0 0.0 0 0.0 717 Micheweni 58 28.6 0 0.0 0 0.0 58 28.6 0 0.0 88 42.9 0 0.0 204 Chakechake 209 52.9 155 39.2 0 0.0 0 0.0 31 7.8 0 0.0 0 0.0 395 Mkoani 214 63.2 36 10.5 0 0.0 36 10.5 54 15.8 0 0.0 0 0.0 339 Total 4,210 45.7 1,237 13.4 313 3.4 1,117 12.1 1,231 13.3 1,089 11.8 25 0.3 9,222 9.8.5 LIVESTOCK EXTENSION: Number of households Receiving Extension advice on Proper Milking and Milk Hygene by District during the 2007/08 Agriculture year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 95 60 0 0 0 0 63 40 0 0 0 0 0 0 158 North 'B' 611 48 0 0 76 6 102 8 280 22 204 16 0 0 1,272 Central 669 47 334 23 61 4 304 21 30 2 30 2 0 0 1,429 South 97 50 16 8 0 0 16 8 16 8 49 25 0 0 195 West 565 33 94 6 63 4 157 9 471 28 314 19 31 2 1,696 Wete 307 71 102 24 0 0 0 0 26 6 0 0 0 0 436 Micheweni 29 25 0 0 0 0 29 25 29 25 29 25 0 0 117 Chakechake 85 41 124 59 0 0 0 0 0 0 0 0 0 0 209 Mkoani 241 51 89 19 0 0 36 8 80 17 27 6 0 0 473 Total 2,700 45 761 13 200 3 707 12 933 16 653 11 31 1 5,984 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 87 9.8.6 LIVESTOCK EXTENSION: Number of households Receiving Extensionadvice on Livestock fattening by District during the 2007/08 Agriculture Year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 126 66.7 32 16.7 0 0.0 0 0.0 0 0.0 32 16.7 0 0.0 189 North 'B' 484 46.3 0 0.0 76 7.3 102 9.8 280 26.8 102 9.8 0 0.0 1,043 Central 365 46.2 182 23.1 61 7.7 91 11.5 30 3.8 61 7.7 0 0.0 790 South 32 50.0 0 0.0 0 0.0 16 25.0 0 0.0 16 25.0 0 0.0 65 West 628 40.0 126 8.0 63 4.0 157 10.0 345 22.0 251 16.0 0 0.0 1,570 Wete 102 57.1 77 42.9 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 179 Micheweni 146 41.7 0 0.0 0 0.0 175 50.0 0 0.0 29 8.3 0 0.0 350 Chakechake 31 25.0 62 50.0 0 0.0 0 0.0 0 0.0 0 0.0 31 25.0 124 Mkoani 107 48.0 36 16.0 0 0.0 0 0.0 80 36.0 0 0.0 0 0.0 223 Total 2,021 44.6 514 11.3 200 4.4 541 11.9 736 16.2 491 10.8 31 0.7 4,535 9.8.7 LIVESTOCK EXTENSION: Number of households receiving extension advice on Disease control (dipping/spraying) by District during the 2007/08 Agriculture year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 630 43.5 126 8.7 0 0.0 221 15.2 126 8.7 347 23.9 0 0.0 1,449 North 'B' 1,374 68.4 0 0.0 76 3.8 102 5.1 280 13.9 178 8.9 0 0.0 2,011 Central 1,489 48.5 426 13.9 122 4.0 334 10.9 213 6.9 486 15.8 0 0.0 3,070 South 666 77.4 32 3.8 49 5.7 16 1.9 49 5.7 49 5.7 0 0.0 861 West 1,413 41.7 314 9.3 94 2.8 314 9.3 691 20.4 502 14.8 63 1.9 3,391 Wete 1,307 72.9 154 8.6 0 0.0 0 0.0 205 11.4 102 5.7 26 1.4 1,794 Micheweni 876 85.7 0 0.0 0 0.0 0 0.0 0 0.0 117 11.4 29 2.9 1,022 Chakechake 574 64.9 124 14.0 0 0.0 0 0.0 155 17.5 31 3.5 0 0.0 884 Mkoani 1,419 85.0 62 3.7 0 0.0 0 0.0 134 8.0 54 3.2 0 0.0 1,669 Total 9,748 60.4 1,238 7.7 341 2.1 987 6.1 1,852 11.5 1,866 11.6 118 0.7 16,150 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 88 9.8.8 LIVESTOCK EXTENSION: Number of households Receiving Extension Advice on Herd/Flock size and Selection by District during the 2007/08 Agriculture Year DISTRICT Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 189 75.0 63 25.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 252 North 'B' 433 58.6 0 0.0 76 10.3 102 13.8 102 13.8 25 3.4 0 0.0 738 Central 456 44.1 243 23.5 0 0.0 213 20.6 91 8.8 30 2.9 0 0.0 1,034 South 49 42.9 16 14.3 0 0.0 32 28.6 16 14.3 0 0.0 0 0.0 114 West 471 34.1 157 11.4 94 6.8 31 2.3 534 38.6 94 6.8 0 0.0 1,382 Wete 179 87.5 0 0.0 0 0.0 0 0.0 26 12.5 0 0.0 0 0.0 205 Micheweni 58 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 58 Chakechake 109 46.7 124 53.3 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 233 Mkoani 268 57.7 62 13.5 0 0.0 54 11.5 80 17.3 0 0.0 0 0.0 464 Total 2,211 49.4 666 14.9 171 3.8 432 9.6 849 19.0 150 3.4 0 0.0 4,479 9.8.9 LIVESTOCK EXTENSION: Number of households Receiving Extension Advice on Pasture Establishment by District during the 2007/08 Agriculture Year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 63 66.7 0 0.0 0 0.0 0 0.0 32 33.3 0 0.0 0 0.0 95 North 'B' 229 40.9 25 4.5 76 13.6 127 22.7 102 18.2 0 0.0 0 0.0 560 Central 365 63.2 152 26.3 0 0.0 61 10.5 0 0.0 0 0.0 0 0.0 578 South 16 20.0 0 0.0 0 0.0 16 20.0 0 0.0 49 60.0 0 0.0 81 West 345 32.4 94 8.8 94 8.8 31 2.9 471 44.1 31 2.9 0 0.0 1,068 Chakechake 54 63.6 31 36.4 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 85 Mkoani 214 60.0 36 10.0 0 0.0 0 0.0 80 22.5 27 7.5 0 0.0 357 Total 1,287 45.6 338 12.0 171 6.0 236 8.3 685 24.3 107 3.8 0 0.0 2,823 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 89 9.8.10 LIVESTOCK EXTENSION: Number of Households Receiving Extension Advice on Group formation and Strengthening by District during the 2007/08 Agriculture year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 284 69.2 95 23.1 0 0.0 0 0.0 0 0.0 32 7.7 0 0.0 410 North 'B' 636 41.0 25 1.6 254 16.4 178 11.5 254 16.4 204 13.1 0 0.0 1,552 Central 395 40.6 365 37.5 91 9.4 30 3.1 61 6.3 30 3.1 0 0.0 973 South 146 52.9 49 17.6 16 5.9 0 0.0 0 0.0 65 23.5 0 0.0 276 West 408 20.0 911 44.6 157 7.7 63 3.1 471 23.1 31 1.5 0 0.0 2,041 Wete 256 55.6 154 33.3 0 0.0 0 0.0 51 11.1 0 0.0 0 0.0 461 Micheweni 88 75.0 29 25.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 117 Chakechake 395 76.1 93 17.9 0 0.0 0 0.0 0 0.0 31 6.0 0 0.0 519 Mkoani 589 77.6 36 4.7 0 0.0 0 0.0 134 17.6 0 0.0 0 0.0 759 Total 3,198 45.0 1,756 24.7 519 7.3 271 3.8 971 13.7 393 5.5 0 0.0 7,108 9.8.11 LIVESTOCK EXTENSION: Number of Households Receiving Extension Advice on Calf Rearing by District during the 2007/08 Agriculture Year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 221 77.8 0 0.0 0 0.0 32 11.1 32 11.1 0 0.0 0 0.0 284 North 'B' 840 51.6 25 1.6 102 6.3 153 9.4 204 12.5 305 18.8 0 0.0 1,629 Central 699 44.2 334 21.2 30 1.9 274 17.3 91 5.8 152 9.6 0 0.0 1,581 South 227 70.0 0 0.0 0 0.0 16 5.0 0 0.0 81 25.0 0 0.0 325 West 565 30.0 345 18.3 126 6.7 126 6.7 534 28.3 157 8.3 31 1.7 1,884 Wete 231 60.0 77 20.0 0 0.0 0 0.0 26 6.7 51 13.3 0 0.0 384 Micheweni 117 44.4 0 0.0 0 0.0 58 22.2 0 0.0 88 33.3 0 0.0 263 Chakechake 295 57.6 124 24.2 0 0.0 0 0.0 62 12.1 31 6.1 0 0.0 512 Mkoani 384 66.2 62 10.8 0 0.0 0 0.0 80 13.8 54 9.2 0 0.0 580 Total 3,578 48.1 969 13.0 258 3.5 658 8.8 1,028 13.8 919 12.4 31 0.4 7,441 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 90 9.8.12 LIVESTOCK EXTENSION: Number of Households Receiving Extension Advice on Use of Improved Bulls by District during the 2007/08 Agriculture Year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 63 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 63 North 'B' 484 47.5 0 0.0 102 10.0 76 7.5 178 17.5 178 17.5 0 0.0 1,018 Central 517 44.7 334 28.9 0 0.0 213 18.4 61 5.3 30 2.6 0 0.0 1,155 South 65 23.5 0 0.0 0 0.0 0 0.0 0 0.0 211 76.5 0 0.0 276 West 534 40.5 251 19.0 94 7.1 0 0.0 314 23.8 126 9.5 0 0.0 1,319 Wete 102 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 102 Micheweni 29 100.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 29 Chakechake 186 54.5 93 27.3 0 0.0 0 0.0 31 9.1 31 9.1 0 0.0 341 Mkoani 268 65.2 36 8.7 0 0.0 0 0.0 107 26.1 0 0.0 0 0.0 411 Total 2,248 47.7 714 15.2 196 4.2 289 6.1 691 14.7 576 12.2 0 0.0 4,714 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 91 9.8.13 LIVESTOCK EXTENSION: Number of Households Receiving Extension Advice on Livestock Feeds Processing by District during the 2007/08 Agriculture Year District Source of Livestock Extension Total Number of households Government NGO/Dev project Cooperative Large scale farmer Radio/TV/Newspapers Neighbour Other Number % Number % Number % Number % Number % Number % Other (Specify) % North 'A' 221 63.6 63 18.2 0 0.0 63 18.2 0 0.0 0 0.0 0 0.0 347 North 'B' 560 41.5 51 3.8 51 3.8 204 15.1 382 28.3 102 7.5 0 0.0 1,349 Central 365 32.4 365 32.4 30 2.7 122 10.8 122 10.8 122 10.8 0 0.0 1,125 South 81 62.5 16 12.5 0 0.0 16 12.5 16 12.5 0 0.0 0 0.0 130 West 471 30.0 220 14.0 94 6.0 63 4.0 565 36.0 157 10.0 0 0.0 1,570 Wete 154 66.7 77 33.3 0 0.0 0 0.0 0 0.0 0 0.0 0 0.0 231 Micheweni 0 0.0 0 0.0 0 0.0 29 100.0 0 0.0 0 0.0 0 0.0 29 Chakechake 132 58.6 62 27.6 0 0.0 0 0.0 31 13.8 0 0.0 0 0.0 225 Mkoani 303 68.0 36 8.0 0 0.0 0 0.0 107 24.0 0 0.0 0 0.0 446 Total 2,286 41.9 889 16.3 175 3.2 496 9.1 1,223 22.4 380 7.0 0 0.0 5,451 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 92 9.9.1 FISH FARMING: Number of Agriculture Households Practising Fish Farming by District during the 2007/08 Agriculture Year District Was Fish farming carried out by this household during 2007/08 Yes % No % Total North 'A' 0 0.0 18,901 100.0 18,901 North 'B' 0 0.0 11,452 100.0 11,452 Central 0 0.0 13,679 100.0 13,679 South 0 0.0 6,580 100.0 6,580 West 0 0.0 18,651 100.0 18,651 Wete 26 0.2 15,349 99.8 15,374 Micheweni 0 0.0 17,520 100.0 17,520 Chakechake 0 0.0 13,835 100.0 13,835 Mkoani 0 0.0 16,199 100.0 16,199 Total 26 0.0 132,168 100.0 132,193 9.9.2 FISH FARMING: Number of Agriculture Households by System of Fish Farming and District during the 2007/08 Agriculture Year District System of fish farming Natural Pond Dug out Pond Water Resevoir Other North 'A' 0 0 0 0 North 'B' 0 0 0 0 Central 0 0 0 0 South 0 0 0 0 West 0 0 0 0 Wete 0 26 0 0 Micheweni 0 0 0 0 Chakechake 0 0 0 0 Mkoani 0 0 0 0 Total 0 26 0 0 9.9.3 FISH FARMING: Number of Agriculture Households by Source of Fingerling by Districts during the 2007/08 Agriculture Year District Source of fingerlings Own Pond Government Institution NGOs / Project Neighbour Private Trader Other Total North 'A' 0 0 0 0 0 0 0 North 'B' 0 0 0 0 0 0 0 Central 0 0 0 0 0 0 0 South 0 0 0 0 0 0 0 West 0 0 0 0 0 0 0 Wete 26 0 0 0 0 0 26 Micheweni 0 0 0 0 0 0 0 Chakechake 0 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 0 Total 26 0 0 0 0 0 26 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 93 9.9.4 FISH FARMING: Number of Agriculture Households by Location of Selling Fish and District during the 2007/08 Agriculture Year District Where sold Neighbour Local Market Secondary Market Processing Industry Large Scale Farm Did not Sell Other Total North 'A' 0 0 0 0 0 0 0 0 North 'B' 0 0 0 0 0 0 0 0 Central 0 0 0 0 0 0 0 0 South 0 0 0 0 0 0 0 0 West 0 0 0 0 0 0 0 0 Wete 0 0 0 0 0 26 0 26 Micheweni 0 0 0 0 0 0 0 0 Chakechake 0 0 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 0 0 Total 0 0 0 0 0 26 0 26 9.9.6 FISH FARMING: Total Number of Stocked Fish by Type and District during 2007/08 agriculture year District Mean Size of Pond (Sq.metre) Type of Fish Total Tilapia Milkfish Prawns/Crabs Lulu Number % Number % Number % Number % North 'A' 0 0 0 0 0 0 0 0 0 0 North 'B' 0 0 0 0 0 0 0 0 0 0 Central 0 0 0 0 0 0 0 0 0 0 South 0 0 0 0 0 0 0 0 0 0 West 0 0 0 0 0 0 0 0 0 0 Wete 10 0 0 0 0 12 0 0 0 12 Micheweni 0 0 0 0 0 0 0 0 0 0 Chakechake 0 0 0 0 0 0 0 0 0 0 Mkoani 0 0 0 0 0 0 0 0 0 0 Total 10 0 0 0 0 12 0 0 0 12 9.9.7 FISH FARMING: Number of Agricultural Households By frequency of stocking of Fingerings in fish ponds and District, 2007/08 Agricultural Year District Frequency of stocking Total 1 2 3 8 North 'A' 0 0 0 0 0 North 'B' 0 0 0 0 0 Central 0 0 0 0 0 South 0 0 0 0 0 West 0 0 0 0 0 Wete 0 26 0 0 26 Micheweni 0 0 0 0 0 Chakechake 0 0 0 0 0 Mkoani 0 0 0 0 0 Total 0 26 0 0 26 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 94 9.9.8 FISH FARMING: Number of Agricultural Households By level of Care of fish Ponds by District, 2007/08 Agricultural Year District Level of care of Fish pond Total High Meadium/Average Low 8 North 'A' 0 0 0 0 0 North 'B' 0 0 0 0 0 Central 0 0 0 0 0 South 0 0 0 0 0 West 0 0 0 0 0 Wete 0 26 0 0 26 Micheweni 0 0 0 0 0 Chakechake 0 0 0 0 0 Mkoani 0 0 0 0 0 Total 0 26 0 0 26 9.10.1 BEE KEEPING: Number of Agricultural Households involved in Honey Production/Collection and District, 2007/08 Agricultural Year District Agricultural Households Involved in Honey Production/Collection Agricultural Households NOT Involved in Honey Production/Collection Total Number % Number % Number % North 'A' 0 0.00 18,901 100.0 18,901 100.0 North 'B' 25 0.22 11,427 99.8 11,452 100.0 Central 91 0.67 13,588 99.3 13,679 100.0 South 244 3.70 6,336 96.3 6,580 100.0 West 31 0.17 18,620 99.8 18,651 100.0 Wete 179 1.17 15,195 98.8 15,374 100.0 Micheweni 350 2.00 17,170 98.0 17,520 100.0 Chakechake 93 0.67 13,742 99.3 13,835 100.0 Mkoani 268 1.65 15,931 98.3 16,199 100.0 Total 1,282 0.97 130,911 99.0 132,193 100.0 9.10.2 BEE KEEPING: Number of Agricultural Households By Honey production/Collection and District , 2007/08 Agricultural Year District Was Honey Harvested? Number of Agricultural Households that Poduced/Collected Honey Number of Agricultural Households that did NOT Poduce/Collect Honey Total Stingless Bee Sting Bee Total Stingless Bee Sting Bee Total Stingless Bee Sting Bee Total North 'B' 0 25 25 0 0 0 0 25 25 Central 61 30 91 0 0 0 61 30 91 South 49 211 260 0 0 0 49 211 260 West 0 31 31 0 0 0 0 31 31 Wete 0 179 179 26 0 26 26 179 205 Micheweni 146 234 380 0 0 0 146 234 380 Chakechake 31 31 62 31 62 93 62 93 155 Mkoani 161 134 295 0 0 0 161 134 295 Total 447 876 1,324 57 62 119 504 938 1,442 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 95 9.10.3 BEE KEEPING: Number of Agricultural Households, type of bee Hives and Type of Bees by District , 2007/08 Agricultural Year District Number of Improved Bee Hives Number of Local Bee Hives Stingless Bee Sting Bee Total Stingless Bee Sting Bee Total No hhds No Hives No hhds No Hives No hhds No Hives No hhds No Hives No hhds No Hives No hhds No Hives North 'B' 0 0 25 0 25 0 0 0 25 0 25 0 Central 61 152 30 0 91 152 61 456 30 152 91 608 South 49 0 211 0 260 0 49 12,055 211 3,249 260 15,304 West 0 0 31 0 31 0 0 0 31 628 31 628 Wete 26 0 179 0 205 0 26 0 179 1,230 205 1,230 Micheweni 146 263 234 0 380 263 146 905 234 2,599 380 3,504 Chakechake 62 62,007 93 0 155 62,007 62 0 93 1,860 155 1,860 Mkoani 161 375 134 0 295 375 161 428 134 1,366 295 1,794 Total 504 62,797 938 0 1,442 62,797 504 13,845 938 11,084 1,442 24,929 9.10.4 BEE KEEPING: Quantity of Honey Harvested and Sold by Size of Bees and District during the 2007/08 Agriculture Year District Stingless Bee Sting Bee Total Honey Harvested Honey Sold Honey Harvested Honey Sold Honey Sold Honey Harvested Quantity (lts) % Quantity (lts) % Quantity (lts) % Quantity (lts) % North 'B' 0 0 0 0 0 0 0 0 0 0 Central 608 71 608 71 243 29 243 29 851 851 South 12,672 53 12,640 55 11,161 47 10,495 45 23,135 23,834 West 0 0 0 0 1,884 100 0 0 0 1,884 Wete 51 4 0 0 1,230 96 922 100 922 1,281 Micheweni 3,533 55 1,694 48 2,862 45 1,840 52 3,533 6,395 Chakechake 0 0 0 0 1,240 0 1,240 0 1,240 1,240 Mkoani 2,222 38 2,142 43 3,641 62 2,811 57 4,953 5,864 Total 19,087 46 17,084 49 22,262 54 17,807 51 34,890 41,349 APPENDIX II Tanzania Agriculture Sample Census - 2007/08 96 9.10.5 BEE KEEPING: Average price of Honey (Tshs/litre) by Size of Bees and District during the 2007/08 Agriculture Year Districts Stingless Bee (Price per Litre) Bee (Price per Litre) Average Price Per Litre North-B 0 3,000 3,000 Central 5,000 1,000 3,500 South 2,033 5,635 6,651 West 0 8,000 8,000 Wete 0 4,429 4,429 Micheweni 5,400 4,912 7,612 Chakechake 0 9,999 9,999 Mkoani 7,500 3,760 7,510 9.10.6 BEE KEEPING: Number of Agriculture Households by Location of Selling Honey and District during the 2007/08 Agriculture Year Districts Neighbour Local market Secondary market Processing industry Large scale farm Trade at farm Did not sell Other Total Stingb ee Stingle ss Bee Stingb ee Stingle ss Bee Stingb ee Stingle ss Bee Stingb ee Stingle ss Bee Stingle ss Bee Stingb ee Stingle ss Bee Stingb ee Stingle ss Bee Stingb ee Stingle ss Bee Stingb ee Stingle ss Bee North-B 25 0 0 0 0 0 0 0 0 0 0 0 0 0 0 25 0 Central 30 0 0 61 0 0 0 0 0 0 0 0 0 0 0 30 61 South 162 16 0 0 16 16 0 0 0 32 16 0 0 0 0 211 49 West 0 0 0 0 0 0 0 0 0 31 0 0 0 0 0 31 0 Wete 102 0 0 0 0 0 0 0 0 0 0 77 26 0 0 179 26 Michewe ni 146 117 29 0 0 0 0 0 0 29 0 29 29 0 0 234 146 Chakecha ke 0 0 0 31 31 0 0 0 0 0 0 0 0 0 0 31 31 Mkoani 134 161 0 0 0 0 0 0 0 0 0 0 0 0 0 134 161 Total 601 294 29 92 47 16 0 0 0 93 16 106 55 0 0 876 473 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 97 APPENDIX III: CENSUS DATA COLLECTION INSTRUMENTS Smallholder Questionnaire Community Questionnaire Village Listing Forms APPENDIX III Tanzania Agriculture Sample Census - 2007/08 98 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 99 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 100 ACLF 3 Region Code ward : code Namba Sawia District village code Hatua Code (1) (5) (6) (7) (8) (9) (10) (11) Poutry (2) (3) (4) Cattle Goat Sheep Pigs UNITED REPUBLIC OF TANZANIA National Agriculture Sample Census 2007/08 Household listing for 15 selected farmers S/N Sub-village leader Number Name of sub-village leader Name of selected head of household Name of a Househol d Head Number of Field CONFIDENTIAL APPENDIX III Tanzania Agriculture Sample Census - 2007/08 101 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 102 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 103 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 104 HOUSEHOLD INFORMATION APPENDIX III Tanzania Agriculture Sample Census - 2007/08 105 CODES FOR Q3: HOUSEHOLD INFORMATION APPENDIX III Tanzania Agriculture Sample Census - 2007/08 106 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 107 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 108 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 109 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 110 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 111 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 112 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 113 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 114 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 115 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 116 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 117 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 118 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 119 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 120 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 121 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 122 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 123 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 124 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 125 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 126 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 127 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 128 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 129 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 130 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 131 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 132 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 133 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 134 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 135 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 136 Appendix V Village Community Level formats APPENDIX III Tanzania Agriculture Sample Census - 2007/08 137 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 138 APPENDIX III Tanzania Agriculture Sample Census - 2007/08 139 ACCESS TO COMMUNAL RESOURCES